System and method for processing image data using an image signal processor having back-end processing logic

ABSTRACT

Disclosed embodiments provide for a an image signal processing system that includes back-end pixel processing unit that receives pixel data after being processed by at least one of a front-end pixel processing unit and a pixel processing pipeline. In certain embodiments, the back-end processing unit receives luma/chroma image data and may be configured to apply face detection operations, local tone mapping, bright, contrast, color adjustments, as well as scaling. Further, the back-end processing unit may also include a back-end statistics unit that may collect frequency statistics. The frequency statistics may be provided to an encoder and may be used to determine quantization parameters that are to be applied to an image frame.

BACKGROUND

The present disclosure relates generally to digital imaging devices and,more particularly, to systems and method for processing image dataobtained using an image sensor of a digital imaging device.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present techniques,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

In recent years, digital imaging devices have become increasing populardue, at least in part, to such devices becoming more and more affordablefor the average consumer. Further, in addition to a number ofstand-alone digital cameras currently available on the market, it is notuncommon for digital imaging devices to be integrated as part of anotherelectronic device, such as a desktop or notebook computer, a cellularphone, or a portable media player.

To acquire image data, most digital imaging devices include an imagesensor that provides a number of light-detecting elements (e.g.,photodetectors) configured to convert light detected by the image sensorinto an electrical signal. An image sensor may also include a colorfilter array that filters light captured by the image sensor to capturecolor information. The image data captured by the image sensor may thenbe processed by an image processing pipeline, which may apply a numberof various image processing operations to the image data to generate afull color image that may be displayed for viewing on a display device,such as a monitor.

While conventional image processing techniques generally aim to producea viewable image that is both objectively and subjectively pleasing to aviewer, such conventional techniques may not adequately address errorsand/or distortions in the image data introduced by the imaging deviceand/or the image sensor. For instance, defective pixels on the imagesensor, which may be due to manufacturing defects or operationalfailure, may fail to sense light levels accurately and, if notcorrected, may manifest as artifacts appearing in the resultingprocessed image. Additionally, light intensity fall-off at the edges ofthe image sensor, which may be due to imperfections in the manufactureof the lens, may adversely affect characterization measurements and mayresult in an image in which the overall light intensity is non-uniform.The image processing pipeline may also perform one or more processes tosharpen the image. Conventional sharpening techniques, however, may notadequately account for existing noise in the image signal, or may beunable to distinguish the noise from edges and textured areas in theimage. In such instances, conventional sharpening techniques mayactually increase the appearance of noise in the image, which isgenerally undesirable. Further, various additional image processingsteps, some of which may rely on image statistics collected by astatistics collection engine, may also be performed.

Another image processing operation that may be applied to the image datacaptured by the image sensor is a demosaicing operation. Because thecolor filter array generally provides color data at one wavelength persensor pixel, a full set of color data is generally interpolated foreach color channel in order to reproduce a full color image (e.g., RGBimage). Conventional demosaicing techniques generally interpolate valuesfor the missing color data in a horizontal or a vertical direction,generally depending on some type of fixed threshold. However, suchconventional demosaicing techniques may not adequately account for thelocations and direction of edges within the image, which may result inedge artifacts, such as aliasing, checkerboard artifacts, or rainbowartifacts, being introduced into the full color image, particularlyalong diagonal edges within the image.

Accordingly, various considerations should be addressed when processinga digital image obtained with a digital camera or other imaging devicein order to improve the appearance of the resulting image. Inparticular, certain aspects of the disclosure below may address one ormore of the drawbacks briefly mentioned above.

SUMMARY

A summary of certain embodiments disclosed herein is set forth below. Itshould be understood that these aspects are presented merely to providethe reader with a brief summary of these certain embodiments and thatthese aspects are not intended to limit the scope of this disclosure.Indeed, this disclosure may encompass a variety of aspects that may notbe set forth below.

The present disclosure provides and illustrates various embodiments ofimage signal processing techniques. Particularly, disclosed embodimentsof this disclosure may relate to the processing of image data using aback-end image processing unit, the arrangement and configuration ofline buffers for implementing raw pixel processing logic, a techniquefor managing the movement of pixel data in the presence of overflow(also called overrun) conditions, techniques for synchronizing video andaudio data, as well as techniques relating to the use of various pixelmemory formats that may be used to store pixel data to memory and toread pixel data from memory.

With regard to back-end processing, disclosed embodiments provide for aan image signal processing system that includes back-end pixelprocessing unit that receives pixel data after being processed by atleast one of a front-end pixel processing unit and a pixel processingpipeline. In certain embodiments, the back-end processing unit receivesluma/chroma image data and may be configured to apply face detectionoperations, local tone mapping, bright, contrast, color adjustments, aswell as scaling. Further, the back-end processing unit may also includea back-end statistics unit that may collect frequency statistics. Thefrequency statistics may be provided to an encoder and may be used todetermine quantization parameters that are to be applied to an imageframe.

A further aspect of the disclosure relates to the implementation of araw pixel processing unit using a set of line buffers. In oneembodiment, the set of line buffers may include a first subset andsecond subset. Various logical units of the raw pixel processing unitmay be implemented using the first and second subsets of line buffers ina shared manner. For instance, in one embodiment, defective pixelcorrection and detection logic may be implemented using the first subsetof line buffers. The second subset of line buffers may be used toimplement lens shading correction logic, gain, offset, and clampinglogic, and demosaicing logic. Further, noise reduction may also beimplemented using at least a portion of each of the first and secondsubsets of line buffers.

Another aspect of the disclosure may relate to an image signalprocessing system includes overflow control logic that detects anoverflow condition when a destination unit when a sensor input queueand/or front-end processing unit receives back pressure from adownstream destination unit. The image signal processing system may alsoinclude a flash controller that is configured to activate a flash deviceprior to the start of a target image frame by using a sensor timingsignal. In one embodiment, the flash controller receives a delayedsensor timing signal and determines a flash activation start time byusing the delayed sensor timing signal to identify a time correspondingto the end of the previous frame, increasing that time by a verticalblanking time, and then subtracting a first offset to compensate fordelay between the sensor timing signal and the delayed sensor timingsignal. Then, the flash controller subtracts a second offset todetermine the flash activation time, thus ensuring that the flash isactivated prior to receiving the first pixel of the target frame.Further aspects of the disclosure provide techniques related toaudio-video synchronization. In one embodiment, a time code registerprovides a current time stamp when sampled. The value of the time coderegister may be incremented at regular intervals based on a clock of theimage signal processing system. At the start of a current frame acquiredby an image sensor, the time code register is sampled, and a timestampis stored into a timestamp register associated with the image sensor.The timestamp is then read from the time stamp register and written to aset of metadata associated with the current frame. The timestamp storedin the frame metadata may then be used to synchronize the current framewith a corresponding set of audio data.

An additional aspect of the present disclosure provides a flexiblememory input/output controller that is configured to the storing andreading of multiple types of pixels and pixel memory formats. Forinstance, the memory I/O controller may support the storing and readingof raw image pixels at various bits of precision, such as 8-bit, 10-bit,12-bit, 14-bit, and 16-bit. Pixel formats that are unaligned with memorybytes (e.g., not being a multiple of 8-bits) may be stored in a packedmanner. The memory I/O controller may also support various formats ofRGB pixel sets and YCC pixel sets.

Various refinements of the features noted above may exist in relation tovarious aspects of the present disclosure. Further features may also beincorporated in these various aspects as well. These refinements andadditional features may exist individually or in any combination. Forinstance, various features discussed below in relation to one or more ofthe illustrated embodiments may be incorporated into any of theabove-described aspects of the present disclosure alone or in anycombination. Again, the brief summary presented above is intended onlyto familiarize the reader with certain aspects and contexts ofembodiments of the present disclosure without limitation to the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee.

Various aspects of this disclosure may be better understood upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is a simplified block diagram depicting components of an exampleof an electronic device that includes an imaging device and imageprocessing circuitry configured to implement one or more of the imageprocessing technique set forth in the present disclosure;

FIG. 2 shows a graphical representation of a 2×2 pixel block of a Bayercolor filter array that may be implemented in the imaging device of FIG.1;

FIG. 3 is a perspective view of the electronic device of FIG. 1 in theform of a laptop computing device, in accordance with aspects of thepresent disclosure;

FIG. 4 is a front view of the electronic device of FIG. 1 in the form ofa desktop computing device, in accordance with aspects of the presentdisclosure;

FIG. 5 is a front view of the electronic device of FIG. 1 in the form ofa handheld portable electronic device, in accordance with aspects of thepresent disclosure;

FIG. 6 is a rear view of the electronic device shown in FIG. 5;

FIG. 7 is a block diagram illustrating an embodiment of the imageprocessing circuitry of FIG. 1 that includes front-end image signalprocessing (ISP) logic and ISP pipe processing logic, in accordance withaspects of the present disclosure;

FIG. 8 is a block diagram illustrating another embodiment of the imageprocessing circuitry of FIG. 1 that includes front-end image signalprocessing (ISP) logic, ISP pipe (pipeline) processing logic, and ISPback-end processing logic, in accordance with aspects of the presentdisclosure;

FIG. 9 is a flow chart depicting methods for processing image data usingeither the image processing circuitry of FIG. 7 or FIG. 8, in accordancewith aspects of the present disclosure;

FIG. 10 is a more detailed block diagram showing an embodiment of theISP front-end logic that may be implemented in FIG. 7 or FIG. 8, inaccordance with aspects of the present disclosure;

FIG. 11 is flow chart depicting a method for processing image data inthe ISP front-end logic of FIG. 10, in accordance with an embodiment

FIG. 12 is block diagram illustrating a configuration of double bufferedregisters and control registers that may be utilized for processingimage data in the ISP front-end logic, in accordance with oneembodiment;

FIGS. 13-15 are timing diagrams depicting different modes for triggeringthe processing of an image frame, in accordance with embodiments of thepresent techniques;

FIG. 16 is a diagram depicting a control register in more detail, inaccordance with one embodiment;

FIG. 17 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 10 is operating in a single sensor mode;

FIG. 18 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 10 is operating in a dual sensor mode;

FIG. 19 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 10 is operating in a dual sensor mode;

FIG. 20 is a flow chart depicting a method in which both image sensorsare active, but wherein a first image sensor is sending image frames toa front-end pixel processing unit, while the second image sensor issending image frames to a statistics processing unit so that imagingstatistics for the second sensor are immediately available when thesecond image sensor continues sending image frames to the front-endpixel processing unit at a later time, in accordance with oneembodiment.

FIG. 21 is a graphical depiction of a linear memory addressing formatthat may be applied to pixel formats stored in a memory of theelectronic device of FIG. 1, in accordance with aspects of the presentdisclosure;

FIG. 22 is a graphical depiction of a tiled memory addressing formatthat may be applied to pixel formats stored in a memory of theelectronic device of FIG. 1, in accordance with aspects of the presentdisclosure;

FIG. 23 is graphical depiction of various imaging regions that may bedefined within a source image frame captured by an image sensor, inaccordance with aspects of the present disclosure;

FIG. 24 is a graphical depiction of a technique for using the ISPfront-end processing unit to process overlapping vertical stripes of animage frame;

FIG. 25 is a diagram depicting how byte swapping may be applied toincoming image pixel data from memory using a swap code, in accordancewith aspects of the present disclosure;

FIGS. 26-29 show examples of memory formats for raw image data that maybe supported by the image processing circuitry of FIG. 7 or FIG. 8, inaccordance with embodiments of the present disclosure;

FIGS. 30-34 show examples of memory formats for full-color RGB imagedata that may be supported by the image processing circuitry of FIG. 7or FIG. 8, in accordance with embodiments of the present disclosure;

FIGS. 35-36 show examples of memory formats for luma/chroma image data(YUV/YC1C2) that may be supported by the image processing circuitry ofFIG. 7 or FIG. 8, in accordance with embodiments of the presentdisclosure;

FIG. 37 shows an example of how to determine a frame location in memoryin a linear addressing format, in accordance with aspects of the presentdisclosure;

FIG. 38 shows an example of how to determine a frame location in memoryin a tile addressing format, in accordance with aspects of the presentdisclosure

FIG. 39 is a block diagram of the ISP circuitry of FIG. 8 depicting howoverflow handling may be performed, in accordance with an embodiment ofthe present disclosure;

FIG. 40 is a flow chart depicting a method for overflow handling when anoverflow condition occurs while image pixel data is being read frompicture memory, in accordance with aspects of the present disclosure;

FIG. 41 is a flow chart depicting a method for overflow handling when anoverflow condition occurs while image pixel data is being read in froman image sensor interface, in accordance with one embodiment of thepresent disclosure;

FIG. 42 is a flow chart depicting another method for overflow handlingwhen an overflow condition occurs while image pixel data is being readin from an image sensor interface, in accordance a further embodiment ofthe present disclosure;

FIG. 43 provides a graphical depiction of image (e.g., video) andcorresponding audio data that may be captured and stored by theelectronic device of FIG. 1;

FIG. 44 illustrates a set of registers that may be used to providetimestamps for synchronizing the audio and video data of FIG. 43, inaccordance with one embodiment;

FIG. 45 is a simplified representation of an image frame that may becaptured as part of the video data of FIG. 43 and showing how timestampinformation may be stored as part of the image frame metadata, inaccordance with aspects of the present disclosure;

FIG. 46 is a flow chart depicting a method for using timestamps basedupon a VSYNC signal to synchronize image data with audio data, inaccordance with one embodiment;

FIG. 47 is a block diagram of the ISP circuitry of FIG. 8 depicting howflash timing control may be performed, in accordance with an embodimentof the present disclosure;

FIG. 48 depicts a technique for determining flash activation anddeactivation times, in accordance with an embodiment of the presentdisclosure;

FIG. 49 is a flow chart depicting a method for determining flashactivation times based on the technique shown in FIG. 48;

FIG. 50 is a flow chart depicting a method for using a pre-flash toupdate image statistics prior to acquisition of an image scene using aflash, in accordance with aspects of the present disclosure;

FIG. 51 is a block diagram that provides a more detailed view of oneembodiment of the ISP front-end pixel processing unit, as shown in theISP front-end logic of FIG. 10, in accordance with aspects of thepresent disclosure;

FIG. 52 is a process diagram illustrating how temporal filtering may beapplied to image pixel data received by the ISP front-end pixelprocessing unit shown in FIG. 51, in accordance with one embodiment;

FIG. 53 illustrates a set of reference image pixels and a set ofcorresponding current image pixels that may be used to determine one ormore parameters for the temporal filtering process shown in FIG. 52;

FIG. 54 is a flow chart illustrating a process for applying temporalfiltering to a current image pixel of a set of image data, in accordancewith one embodiment;

FIG. 55 is a flow chart showing a technique for calculating a motiondelta value for use with the temporal filtering of the current imagepixel of FIG. 54, in accordance with one embodiment;

FIG. 56 is a flow chart illustrating another process for applyingtemporal filtering to a current image pixel of a set of image data thatincludes the use of different gains for each color component of theimage data, in accordance with another embodiment;

FIG. 57 is a process diagram illustrating a how a temporal filteringtechnique that utilizes separate motion and luma tables for each colorcomponent of the image pixel data received by the ISP front-end pixelprocessing unit shown in FIG. 51, in accordance with a furtherembodiment;

FIG. 58 is a flow chart illustrating a process for applying temporalfiltering to a current image pixel of a set of image data using themotion and luma tables shown in FIG. 57, in accordance with furtherembodiment;

FIG. 59 depicts a sample of full resolution raw image data that may becaptured by an image sensor, in accordance with aspects of the presentdisclosure;

FIG. 60 illustrates an image sensor that may be configured to applybinning to the full resolution raw image data of FIG. 59 to output asample of binned raw image data, in accordance with an embodiment of thepresent disclosure;

FIG. 61 depicts a sample of binned raw image data that may be providedby the image sensor of FIG. 60, in accordance with aspects of thepresent disclosure;

FIG. 62 depicts the binned raw image data from FIG. 61 after beingre-sampled by a binning compensation filter to provide, in accordancewith aspects of the present disclosure;

FIG. 63 depicts a binning compensation filter that may be implemented inthe ISP front-end pixel processing unit of FIG. 51, in accordance withone embodiment;

FIG. 64 is a graphical depiction of various step sizes that may beapplied to a differential analyzer to select center input pixels andindex/phases for binning compensation filtering, in accordance withaspects of the present disclosure;

FIG. 65 is a flow chart illustrating a process for scaling image datausing the binning compensation filter of FIG. 63, in accordance with oneembodiment;

FIG. 66 is a flow chart illustrating a process for determining a currentinput source center pixel for horizontal and vertical filtering by thebinning compensation filter of FIG. 63, in accordance with oneembodiment;

FIG. 67 is a flow chart illustrating a process for determining an indexfor selecting filtering coefficients for horizontal and verticalfiltering by the binning compensation filter of FIG. 63, in accordancewith one embodiment.

FIG. 68 is more a more detailed block diagram showing an embodiment of astatistics processing unit which may be implemented in the ISP front-endprocessing logic, as shown in FIG. 10, in accordance with aspects of thepresent disclosure;

FIG. 69 shows various image frame boundary cases that may be consideredwhen applying techniques for detecting and correcting defective pixelsduring statistics processing by the statistics processing unit of FIG.68, in accordance with aspects of the present disclosure;

FIG. 70 is a flow chart illustrating a process for performing defectivepixel detection and correction during statistics processing, inaccordance with one embodiment;

FIG. 71 shows a three-dimensional profile depicting light intensityversus pixel position for a conventional lens of an imaging device;

FIG. 72 is a colored drawing that exhibits non-uniform light intensityacross the image, which may be the result of lens shadingirregularities;

FIG. 73 is a graphical illustration of a raw imaging frame that includesa lens shading correction region and a gain grid, in accordance withaspects of the present disclosure;

FIG. 74 illustrates the interpolation of a gain value for an image pixelenclosed by four bordering grid gain points, in accordance with aspectsof the present disclosure;

FIG. 75 is a flow chart illustrating a process for determininginterpolated gain values that may be applied to imaging pixels during alens shading correction operation, in accordance with an embodiment ofthe present technique;

FIG. 76 is a three-dimensional profile depicting interpolated gainvalues that may be applied to an image that exhibits the light intensitycharacteristics shown in FIG. 71 when performing lens shadingcorrection, in accordance with aspects of the present disclosure;

FIG. 77 shows the colored drawing from FIG. 72 that exhibits improveduniformity in light intensity after a lens shading correction operationis applied, in accordance with accordance aspects of the presentdisclosure;

FIG. 78 graphically illustrates how a radial distance between a currentpixel and the center of an image may be calculated and used to determinea radial gain component for lens shading correction, in accordance withone embodiment;

FIG. 79 is a flow chart illustrating a process by which radial gains andinterpolated gains from a gain grid are used to determine a total gainthat may be applied to imaging pixels during a lens shading correctionoperation, in accordance with an embodiment of the present technique;

FIG. 80 is a graph showing white areas and low and high colortemperature axes in a color space;

FIG. 81 is a table showing how white balance gains may be configured forvarious reference illuminant conditions, in accordance with oneembodiment;

FIG. 82 is a block diagram showing a statistics collection engine thatmay be implemented in the ISP front-end processing logic, in accordancewith an embodiment of the present disclosure;

FIG. 83 illustrates the down-sampling of raw Bayer RGB data, inaccordance with aspects of the present disclosure;

FIG. 84 depicts a two-dimensional color histogram that may be collectedby the statistics collection engine of FIG. 82, in accordance with oneembodiment;

FIG. 85 depicts zooming and panning within a two-dimensional colorhistogram;

FIG. 86 is a more detailed view showing logic for implementing a pixelfilter of the statistics collection engine, in accordance with oneembodiment;

FIG. 87 is a graphical depiction of how the location of a pixel within aC₁-C₂ color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with one embodiment;

FIG. 88 is a graphical depiction of how the location of a pixel within aC₁-C₂ color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with another embodiment;

FIG. 89 is a graphical depiction of how the location of a pixel within aC₁-C₂ color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with yet a further embodiment;

FIG. 90 is a graph showing how image sensor integration times may bedetermined to compensate for flicker, in accordance with one embodiment;

FIG. 91 is a detailed block diagram showing logic that may beimplemented in the statistics collection engine of FIG. 82 andconfigured to collect auto-focus statistics in accordance with oneembodiment;

FIG. 92 is a graph depicting a technique for performing auto-focus usingcoarse and fine auto-focus scoring values, in accordance with oneembodiment;

FIG. 93 is a flow chart depicting a process for performing auto-focususing coarse and fine auto-focus scoring values, in accordance with oneembodiment;

FIGS. 94 and 95 show the decimation of raw Bayer data to obtain a whitebalanced luma value;

FIG. 96 shows a technique for performing auto-focus using relativeauto-focus scoring values for each color component, in accordance withone embodiment;

FIG. 97 is a more detailed view of the statistics processing unit ofFIG. 68, showing how Bayer RGB histogram data may be used to assistblack level compensation, in accordance with one embodiment;

FIG. 98 is a block diagram showing an embodiment of the ISP pipeprocessing logic of FIG. 7, in accordance with aspects of the presentdisclosure;

FIG. 99 is a more detailed view showing an embodiment of a raw pixelprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 98, in accordance with aspects of the present disclosure;

FIG. 100 shows various image frame boundary cases that may be consideredwhen applying techniques for detecting and correcting defective pixelsduring processing by the raw pixel processing block shown in FIG. 99, inaccordance with aspects of the present disclosure;

FIGS. 101-103 are flowcharts that depict various processes for detectingand correcting defective pixels that may be performed in the raw pixelprocessing block of FIG. 99, in accordance with one embodiment;

FIG. 104 shows the location of two green pixels in a 2×2 pixel block ofa Bayer image sensor that may be interpolated when applying greennon-uniformity correction techniques during processing by the raw pixelprocessing logic of FIG. 99, in accordance with aspects of the presentdisclosure;

FIG. 105 illustrates a set of pixels that includes a center pixel andassociated horizontal neighboring pixels that may be used as part of ahorizontal filtering process for noise reduction, in accordance withaspects of the present disclosure;

FIG. 106 illustrates a set of pixels that includes a center pixel andassociated vertical neighboring pixels that may be used as part of avertical filtering process for noise reduction, in accordance withaspects of the present disclosure;

FIG. 107 is a simplified flow diagram that depicts how demosaicing maybe applied to a raw Bayer image pattern to produce a full color RGBimage;

FIG. 108 depicts a set of pixels of a Bayer image pattern from whichhorizontal and vertical energy components may be derived forinterpolating green color values during demosaicing of the Bayer imagepattern, in accordance with one embodiment;

FIG. 109 shows a set of horizontal pixels to which filtering may beapplied to determine a horizontal component of an interpolated greencolor value during demosaicing of a Bayer image pattern, in accordancewith aspects of the present technique;

FIG. 110 shows a set of vertical pixels to which filtering may beapplied to determine a vertical component of an interpolated green colorvalue during demosaicing of a Bayer image pattern, in accordance withaspects of the present technique;

FIG. 111 shows various 3×3 pixel blocks to which filtering may beapplied to determine interpolated red and blue values during demosaicingof a Bayer image pattern, in accordance with aspects of the presenttechnique;

FIGS. 112-115 provide flowcharts that depict various processes forinterpolating green, red, and blue color values during demosaicing of aBayer image pattern, in accordance with one embodiment;

FIG. 116 shows a colored drawing of an original image scene that may becaptured by an image sensor and processed in accordance with aspects ofthe demosaicing techniques disclosed herein;

FIG. 117 shows a colored drawing of Bayer image pattern of the imagescene shown in FIG. 116;

FIG. 118 shows a colored drawing of an RGB image reconstructed using aconventional demosaicing technique based upon the Bayer image pattern ofFIG. 117;

FIG. 119 shows a colored drawing of an RGB image reconstructed from theBayer image pattern of FIG. 117 in accordance with aspects of thedemosaicing techniques disclosed herein;

FIGS. 120-123 depict a configuration and arrangement of line buffersthat may be used in implementing the raw pixel processing block of FIG.99, in accordance with one embodiment;

FIG. 124 is a flowchart showing a method for processing raw pixel datausing the line buffer configuration shown in FIGS. 120-123, inaccordance with one embodiment;

FIG. 125 is a more detailed view showing one embodiment of an RGBprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 98, in accordance with aspects of the present disclosure;

FIG. 126 is a more detailed view showing one embodiment of a YCbCrprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 98, in accordance with aspects of the present disclosure;

FIG. 127 is a graphical depiction of active source regions for luma andchroma, as defined within a source buffer using a 1-plane format, inaccordance with aspects of the present disclosure;

FIG. 128 is a graphical depiction of active source regions for luma andchroma, as defined within a source buffer using a 2-plane format, inaccordance with aspects of the present disclosure;

FIG. 129 is a block diagram illustrating image sharpening logic that maybe implemented in the YCbCr processing block, as shown in FIG. 126, inaccordance with one embodiment;

FIG. 130 is a block diagram illustrating edge enhancement logic that maybe implemented in the YCbCr processing block, as shown in FIG. 126, inaccordance with one embodiment;

FIG. 131 is a graph showing the relationship of chroma attenuationfactors to sharpened luma values, in accordance with aspects of thepresent disclosure;

FIG. 132 is a block diagram illustrating image brightness, contrast, andcolor (BCC) adjustment logic that may be implemented in the YCbCrprocessing block, as shown in FIG. 126, in accordance with oneembodiment;

FIG. 133 shows a hue and saturation color wheel in the YCbCr color spacedefining various hue angles and saturation values that may be appliedduring color adjustment in the BCC adjustment logic shown in FIG. 132;

FIG. 134 is a block diagram showing an embodiment of the ISP back-endprocessing logic of FIG. 8 that may be configured to perform variouspost-processing steps downstream of the ISP pipeline, in accordance withaspects of the present disclosure;

FIG. 135 is a graphical illustration showing a conventional global tonemapping technique;

FIG. 136 is a graphical illustration showing another conventional globaltone mapping technique;

FIG. 137 depicts how regions of an image may be segmented forapplication of local tone application techniques, in accordance withaspects of the present disclosure;

FIG. 138 graphically illustrates how conventional local tone mapping mayresult in limited utilization of an output tone range;

FIG. 139 graphically illustrates a technique for local tone mapping, inaccordance with embodiments of the present disclosure;

FIG. 140 is a more detailed block diagram showing an embodiment of localtone mapping LTM logic that may be configured to implement tone mappingprocesses in the ISP back-end logic of FIG. 134, in accordance aspectsof the present disclosure;

FIG. 141 is a flow chart showing a method for processing image datausing the ISP back-end processing logic of FIG. 134, in accordance withone embodiment; and

FIG. 142 is a flow chart showing a method for applying tone-mappingusing the LTM logic shown in FIG. 140, in accordance with oneembodiment.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

One or more specific embodiments of the present disclosure will bedescribed below. These described embodiments are only examples of thepresently disclosed techniques. Additionally, in an effort to provide aconcise description of these embodiments, all features of an actualimplementation may not be described in the specification. It should beappreciated that in the development of any such actual implementation,as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Additionally, it should be understood that references to “oneembodiment” or “an embodiment” of the present disclosure are notintended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

As will be discussed below, the present disclosure relates generally totechniques for processing image data acquired via one or more imagesensing devices. In particular, certain aspects of the presentdisclosure may relate to techniques for detecting and correctingdefective pixels, techniques for demosaicing a raw image pattern,techniques for sharpening a luminance image using a multi-scale unsharpmask, and techniques for applying lens shading gains to correct for lensshading irregularities. Further, it should be understood that thepresently disclosed techniques may be applied to both still images andmoving images (e.g., video), and may be utilized in any suitable type ofimaging application, such as a digital camera, an electronic devicehaving an integrated digital camera, a security or video surveillancesystem, a medical imaging system, and so forth.

Keeping the above points in mind, FIG. 1 is a block diagram illustratingan example of an electronic device 10 that may provide for theprocessing of image data using one or more of the image processingtechniques briefly mentioned above. The electronic device 10 may be anytype of electronic device, such as a laptop or desktop computer, amobile phone, a digital media player, or the like, that is configured toreceive and process image data, such as data acquired using one or moreimage sensing components. By way of example only, the electronic device10 may be a portable electronic device, such as a model of an iPod® oriPhone®, available from Apple Inc. of Cupertino, Calif. Additionally,the electronic device 10 may be a desktop or laptop computer, such as amodel of a MacBook®, MacBook® Pro, MacBook Air®, iMac®, Mac® Mini, orMac Pro®, available from Apple Inc. In other embodiments, electronicdevice 10 may also be a model of an electronic device from anothermanufacturer that is capable of acquiring and processing image data.

Regardless of its form (e.g., portable or non-portable), it should beunderstood that the electronic device 10 may provide for the processingof image data using one or more of the image processing techniquesbriefly discussed above, which may include defective pixel correctionand/or detection techniques, lens shading correction techniques,demosaicing techniques, or image sharpening techniques, among others. Insome embodiments, the electronic device 10 may apply such imageprocessing techniques to image data stored in a memory of the electronicdevice 10. In further embodiments, the electronic device 10 may includeone or more imaging devices, such as an integrated or external digitalcamera, configured to acquire image data, which may then be processed bythe electronic device 10 using one or more of the above-mentioned imageprocessing techniques. Embodiments showing both portable andnon-portable embodiments of electronic device 10 will be furtherdiscussed below in FIGS. 3-6.

As shown in FIG. 1, the electronic device 10 may include variousinternal and/or external components which contribute to the function ofthe device 10. Those of ordinary skill in the art will appreciate thatthe various functional blocks shown in FIG. 1 may comprise hardwareelements (including circuitry), software elements (including computercode stored on a computer-readable medium) or a combination of bothhardware and software elements. For example, in the presentlyillustrated embodiment, the electronic device 10 may includeinput/output (I/O) ports 12, input structures 14, one or more processors16, memory device 18, non-volatile storage 20, expansion card(s) 22,networking device 24, power source 26, and display 28. Additionally, theelectronic device 10 may include one or more imaging devices 30, such asa digital camera, and image processing circuitry 32. As will bediscussed further below, the image processing circuitry 32 may beconfigured implement one or more of the above-discussed image processingtechniques when processing image data. As can be appreciated, image dataprocessed by image processing circuitry 32 may be retrieved from thememory 18 and/or the non-volatile storage device(s) 20, or may beacquired using the imaging device 30.

Before continuing, it should be understood that the system block diagramof the device 10 shown in FIG. 1 is intended to be a high-level controldiagram depicting various components that may be included in such adevice 10. That is, the connection lines between each individualcomponent shown in FIG. 1 may not necessarily represent paths ordirections through which data flows or is transmitted between variouscomponents of the device 10. Indeed, as discussed below, the depictedprocessor(s) 16 may, in some embodiments, include multiple processors,such as a main processor (e.g., CPU), and dedicated image and/or videoprocessors. In such embodiments, the processing of image data may beprimarily handled by these dedicated processors, thus effectivelyoffloading such tasks from a main processor (CPU).

With regard to each of the illustrated components in FIG. 1, the I/Oports 12 may include ports configured to connect to a variety ofexternal devices, such as a power source, an audio output device (e.g.,headset or headphones), or other electronic devices (such as handhelddevices and/or computers, printers, projectors, external displays,modems, docking stations, and so forth). In one embodiment, the I/Oports 12 may be configured to connect to an external imaging device,such as a digital camera, for the acquisition of image data that may beprocessed using the image processing circuitry 32. The I/O ports 12 maysupport any suitable interface type, such as a universal serial bus(USB) port, a serial connection port, an IEEE-1394 (FireWire) port, anEthernet or modem port, and/or an AC/DC power connection port.

In some embodiments, certain I/O ports 12 may be configured to providefor more than one function. For instance, in one embodiment, the I/Oports 12 may include a proprietary port from Apple Inc. that mayfunction not only to facilitate the transfer of data between theelectronic device 10 and an external source, but also to couple thedevice 10 to a power charging interface such as an power adapterdesigned to provide power from a electrical wall outlet, or an interfacecable configured to draw power from another electrical device, such as adesktop or laptop computer, for charging the power source 26 (which mayinclude one or more rechargeable batteries). Thus, the I/O port 12 maybe configured to function dually as both a data transfer port and anAC/DC power connection port depending, for example, on the externalcomponent being coupled to the device 10 via the I/O port 12.

The input structures 14 may provide user input or feedback to theprocessor(s) 16. For instance, input structures 14 may be configured tocontrol one or more functions of electronic device 10, such asapplications running on electronic device 10. By way of example only,input structures 14 may include buttons, sliders, switches, controlpads, keys, knobs, scroll wheels, keyboards, mice, touchpads, and soforth, or some combination thereof. In one embodiment, input structures14 may allow a user to navigate a graphical user interface (GUI)displayed on device 10. Additionally, input structures 14 may include atouch sensitive mechanism provided in conjunction with display 28. Insuch embodiments, a user may select or interact with displayed interfaceelements via the touch sensitive mechanism.

The input structures 14 may include the various devices, circuitry, andpathways by which user input or feedback is provided to one or moreprocessors 16. Such input structures 14 may be configured to control afunction of the device 10, applications running on the device 10, and/orany interfaces or devices connected to or used by the electronic device10. For example, the input structures 14 may allow a user to navigate adisplayed user interface or application interface. Examples of the inputstructures 14 may include buttons, sliders, switches, control pads,keys, knobs, scroll wheels, keyboards, mice, touchpads, and so forth.

In certain embodiments, an input structure 14 and the display device 28may be provided together, such as in the case of a “touchscreen,”whereby a touch-sensitive mechanism is provided in conjunction with thedisplay 28. In such embodiments, the user may select or interact withdisplayed interface elements via the touch-sensitive mechanism. In thisway, the displayed interface may provide interactive functionality,allowing a user to navigate the displayed interface by touching thedisplay 28. For example, user interaction with the input structures 14,such as to interact with a user or application interface displayed onthe display 26, may generate electrical signals indicative of the userinput. These input signals may be routed via suitable pathways, such asan input hub or data bus, to the one or more processors 16 for furtherprocessing.

In one embodiment, the input structures 14 may include an audio inputdevice. For instance, one or more audio captures devices, such as one ormore microphones, may be provided with the electronic device 10. Theaudio capture devices may be integrated with the electronic device 10 ormay be an external device coupled to the electronic device 10, such asby way of the I/O ports 12. As discussed further below, the electronicdevice 10 may both an audio input device and imaging device 30 tocapture sound and image data (e.g., video data), and may include logicconfigured to provide for synchronization of the captured video andaudio data.

In addition to processing various input signals received via the inputstructure(s) 14, the processor(s) 16 may control the general operationof the device 10. For instance, the processor(s) 16 may provide theprocessing capability to execute an operating system, programs, user andapplication interfaces, and any other functions of the electronic device10. The processor(s) 16 may include one or more microprocessors, such asone or more “general-purpose” microprocessors, one or morespecial-purpose microprocessors and/or application-specificmicroprocessors (ASICs), or a combination of such processing components.For example, the processor(s) 16 may include one or more instruction set(e.g., RISC) processors, as well as graphics processors (GPU), videoprocessors, audio processors and/or related chip sets. As will beappreciated, the processor(s) 16 may be coupled to one or more databuses for transferring data and instructions between various componentsof the device 10. In certain embodiments, the processor(s) 16 mayprovide the processing capability to execute an imaging applications onthe electronic device 10, such as Photo Booth®, Aperture®, iPhoto®, orPreview®, available from Apple Inc., or the “Camera” and/or “Photo”applications provided by Apple Inc. and available on models of theiPhone®.

The instructions or data to be processed by the processor(s) 16 may bestored in a computer-readable medium, such as a memory device 18. Thememory device 18 may be provided as a volatile memory, such as randomaccess memory (RAM) or as a non-volatile memory, such as read-onlymemory (ROM), or as a combination of one or more RAM and ROM devices.The memory 18 may store a variety of information and may be used forvarious purposes. For example, the memory 18 may store firmware for theelectronic device 10, such as a basic input/output system (BIOS), anoperating system, various programs, applications, or any other routinesthat may be executed on the electronic device 10, including userinterface functions, processor functions, and so forth. In addition, thememory 18 may be used for buffering or caching during operation of theelectronic device 10. For instance, in one embodiment, the memory 18include one or more frame buffers for buffering video data as it isbeing output to the display 28.

In addition to the memory device 18, the electronic device 10 mayfurther include a non-volatile storage 20 for persistent storage of dataand/or instructions. The non-volatile storage 20 may include flashmemory, a hard drive, or any other optical, magnetic, and/or solid-statestorage media, or some combination thereof. Thus, although depicted as asingle device in FIG. 1 for purposes of clarity, it should understoodthat the non-volatile storage device(s) 20 may include a combination ofone or more of the above-listed storage devices operating in conjunctionwith the processor(s) 16. The non-volatile storage 20 may be used tostore firmware, data files, image data, software programs andapplications, wireless connection information, personal information,user preferences, and any other suitable data. In accordance withaspects of the present disclosure, image data stored in the non-volatilestorage 20 and/or the memory device 18 may be processed by the imageprocessing circuitry 32 prior to being output on a display.

The embodiment illustrated in FIG. 1 may also include one or more cardor expansion slots. The card slots may be configured to receive anexpansion card 22 that may be used to add functionality, such asadditional memory, I/O functionality, or networking capability, to theelectronic device 10. Such an expansion card 22 may connect to thedevice through any type of suitable connector, and may be accessedinternally or external with respect to a housing of the electronicdevice 10. For example, in one embodiment, the expansion card 24 may beflash memory card, such as a SecureDigital (SD) card, mini- or microSD,CompactFlash card, or the like, or may be a PCMCIA device. Additionally,the expansion card 24 may be a Subscriber Identity Module (SIM) card,for use with an embodiment of the electronic device 10 that providesmobile phone capability.

The electronic device 10 also includes the network device 24, which maybe a network controller or a network interface card (NIC) that mayprovide for network connectivity over a wireless 802.11 standard or anyother suitable networking standard, such as a local area network (LAN),a wide area network (WAN), such as an Enhanced Data Rates for GSMEvolution (EDGE) network, a 3G data network, or the Internet. In certainembodiments, the network device 24 may provide for a connection to anonline digital media content provider, such as the iTunes® musicservice, available from Apple Inc.

The power source 26 of the device 10 may include the capability to powerthe device 10 in both non-portable and portable settings. For example,in a portable setting, the device 10 may include one or more batteries,such as a L1-Ion battery, for powering the device 10. The battery may bere-charged by connecting the device 10 to an external power source, suchas to an electrical wall outlet. In a non-portable setting, the powersource 26 may include a power supply unit (PSU) configured to draw powerfrom an electrical wall outlet, and to distribute the power to variouscomponents of a non-portable electronic device, such as a desktopcomputing system.

The display 28 may be used to display various images generated by device10, such as a GUI for an operating system, or image data (includingstill images and video data) processed by the image processing circuitry32, as will be discussed further below. As mentioned above, the imagedata may include image data acquired using the imaging device 30 orimage data retrieved from the memory 18 and/or non-volatile storage 20.The display 28 may be any suitable type of display, such as a liquidcrystal display (LCD), plasma display, or an organic light emittingdiode (OLED) display, for example. Additionally, as discussed above, thedisplay 28 may be provided in conjunction with the above-discussedtouch-sensitive mechanism (e.g., a touch screen) that may function aspart of a control interface for the electronic device 10.

The illustrated imaging device(s) 30 may be provided as a digital cameraconfigured to acquire both still images and moving images (e.g., video).The camera 30 may include a lens and one or more image sensorsconfigured to capturing and converting light into electrical signals. Byway of example only, the image sensor may include a CMOS image sensor(e.g., a CMOS active-pixel sensor (APS)) or a CCD (charge-coupleddevice) sensor. Generally, the image sensor in the camera 30 includes anintegrated circuit having an array of pixels, wherein each pixelincludes a photodetector for sensing light. As those skilled in the artwill appreciate, the photodetectors in the imaging pixels generallydetect the intensity of light captured via the camera lenses. However,photodetectors, by themselves, are generally unable to detect thewavelength of the captured light and, thus, are unable to determinecolor information.

Accordingly, the image sensor may further include a color filter array(CFA) that may overlay or be disposed over the pixel array of the imagesensor to capture color information. The color filter array may includean array of small color filters, each of which may overlap a respectivepixel of the image sensor and filter the captured light by wavelength.Thus, when used in conjunction, the color filter array and thephotodetectors may provide both wavelength and intensity informationwith regard to light captured through the camera, which may berepresentative of a captured image.

In one embodiment, the color filter array may include a Bayer colorfilter array, which provides a filter pattern that is 50% greenelements, 25% red elements, and 25% blue elements. For instance, FIG. 2shows a 2×2 pixel block of a Bayer CFA includes 2 green elements (Gr andGb), 1 red element (R), and 1 blue element (B). Thus, an image sensorthat utilizes a Bayer color filter array may provide informationregarding the intensity of the light received by the camera 30 at thegreen, red, and blue wavelengths, whereby each image pixel records onlyone of the three colors (RGB). This information, which may be referredto as “raw image data” or data in the “raw domain,” may then beprocessed using one or more demosaicing techniques to convert the rawimage data into a full color image, generally by interpolating a set ofred, green, and blue values for each pixel. As will be discussed furtherbelow, such demosaicing techniques may be performed by the imageprocessing circuitry 32.

As mentioned above, the image processing circuitry 32 may provide forvarious image processing steps, such as defective pixeldetection/correction, lens shading correction, demosaicing, and imagesharpening, noise reduction, gamma correction, image enhancement,color-space conversion, image compression, chroma sub-sampling, andimage scaling operations, and so forth. In some embodiments, the imageprocessing circuitry 32 may include various subcomponents and/ordiscrete units of logic that collectively form an image processing“pipeline” for performing each of the various image processing steps.These subcomponents may be implemented using hardware (e.g., digitalsignal processors or ASICs) or software, or via a combination ofhardware and software components. The various image processingoperations that may be provided by the image processing circuitry 32and, particularly those processing operations relating to defectivepixel detection/correction, lens shading correction, demosaicing, andimage sharpening, will be discussed in greater detail below.

Before continuing, it should be noted that while various embodiments ofthe various image processing techniques discussed below may utilize aBayer CFA, the presently disclosed techniques are not intended to belimited in this regard. Indeed, those skilled in the art will appreciatethat the image processing techniques provided herein may be applicableto any suitable type of color filter array, including RGBW filters, CYGMfilters, and so forth.

Referring again to the electronic device 10, FIGS. 3-6 illustratevarious forms that the electronic device 10 may take. As mentionedabove, the electronic device 10 may take the form of a computer,including computers that are generally portable (such as laptop,notebook, and tablet computers) as well as computers that are generallynon-portable (such as desktop computers, workstations and/or servers),or other type of electronic device, such as handheld portable electronicdevices (e.g., digital media player or mobile phone). In particular,FIGS. 3 and 4 depict the electronic device 10 in the form of a laptopcomputer 40 and a desktop computer 50, respectively. FIGS. 5 and 6 showfront and rear views, respectively, of the electronic device 10 in theform of a handheld portable device 60.

As shown in FIG. 3, the depicted laptop computer 40 includes a housing42, the display 28, the I/O ports 12, and the input structures 14. Theinput structures 14 may include a keyboard and a touchpad mouse that areintegrated with the housing 42. Additionally, the input structure 14 mayinclude various other buttons and/or switches which may be used tointeract with the computer 40, such as to power on or start thecomputer, to operate a GUI or an application running on the computer 40,as well as adjust various other aspects relating to operation of thecomputer 40 (e.g., sound volume, display brightness, etc.). The computer40 may also include various I/O ports 12 that provide for connectivityto additional devices, as discussed above, such as a FireWire® or USBport, a high definition multimedia interface (HDMI) port, or any othertype of port that is suitable for connecting to an external device.Additionally, the computer 40 may include network connectivity (e.g.,network device 26), memory (e.g., memory 20), and storage capabilities(e.g., storage device 22), as described above with respect to FIG. 1.

Further, the laptop computer 40, in the illustrated embodiment, mayinclude an integrated imaging device 30 (e.g., camera). In otherembodiments, the laptop computer 40 may utilize an external camera(e.g., an external USB camera or a “webcam”) connected to one or more ofthe I/O ports 12 instead of or in addition to the integrated camera 30.For instance, an external camera may be an iSight® camera available fromApple Inc. The camera 30, whether integrated or external, may providefor the capture and recording of images. Such images may then be viewedby a user using an image viewing application, or may be utilized byother applications, including video-conferencing applications, such asiChat®, and image editing/viewing applications, such as Photo Booth®,Aperture®, iPhoto®, or Preview®, which are available from Apple Inc. Incertain embodiments, the depicted laptop computer 40 may be a model of aMacBook®, MacBook® Pro, MacBook Air®, or PowerBook® available from AppleInc. Additionally, the computer 40, in one embodiment, may be a portabletablet computing device, such as a model of an iPad® tablet computer,also available from Apple Inc.

FIG. 4 further illustrates an embodiment in which the electronic device10 is provided as a desktop computer 50. As will be appreciated, thedesktop computer 50 may include a number of features that may begenerally similar to those provided by the laptop computer 40 shown inFIG. 4, but may have a generally larger overall form factor. As shown,the desktop computer 50 may be housed in an enclosure 42 that includesthe display 28, as well as various other components discussed above withregard to the block diagram shown in FIG. 1. Further, the desktopcomputer 50 may include an external keyboard and mouse (input structures14) that may be coupled to the computer 50 via one or more I/O ports 12(e.g., USB) or may communicate with the computer 50 wirelessly (e.g.,RF, Bluetooth, etc.). The desktop computer 50 also includes an imagingdevice 30, which may be an integrated or external camera, as discussedabove. In certain embodiments, the depicted desktop computer 50 may be amodel of an iMac®, Mac® mini, or Mac Pro®, available from Apple Inc.

As further shown, the display 28 may be configured to generate variousimages that may be viewed by a user. For example, during operation ofthe computer 50, the display 28 may display a graphical user interface(“GUI”) 52 that allows the user to interact with an operating systemand/or application running on the computer 50. The GUI 52 may includevarious layers, windows, screens, templates, or other graphical elementsthat may be displayed in all, or a portion, of the display device 28.For instance, in the depicted embodiment, an operating system GUI 52 mayinclude various graphical icons 54, each of which may correspond tovarious applications that may be opened or executed upon detecting auser selection (e.g., via keyboard/mouse or touchscreen input). Theicons 54 may be displayed in a dock 56 or within one or more graphicalwindow elements 58 displayed on the screen. In some embodiments, theselection of an icon 54 may lead to a hierarchical navigation process,such that selection of an icon 54 leads to a screen or opens anothergraphical window that includes one or more additional icons or other GUIelements. By way of example only, the operating system GUI 52 displayedin FIG. 4 may be from a version of the Mac OS® operating system,available from Apple Inc.

Continuing to FIGS. 5 and 6, the electronic device 10 is furtherillustrated in the form of portable handheld electronic device 60, whichmay be a model of an iPod® or iPhone® available from Apple Inc. In thedepicted embodiment, the handheld device 60 includes an enclosure 42,which may function to protect the interior components from physicaldamage and to shield them from electromagnetic interference. Theenclosure 42 may be formed from any suitable material or combination ofmaterials, such as plastic, metal, or a composite material, and mayallow certain frequencies of electromagnetic radiation, such as wirelessnetworking signals, to pass through to wireless communication circuitry(e.g., network device 24), which may be disposed within the enclosure42, as shown in FIG. 5.

The enclosure 42 also includes various user input structures 14 throughwhich a user may interface with the handheld device 60. For instance,each input structure 14 may be configured to control one or morerespective device functions when pressed or actuated. By way of example,one or more of the input structures 14 may be configured to invoke a“home” screen 42 or menu to be displayed, to toggle between a sleep,wake, or powered on/off mode, to silence a ringer for a cellular phoneapplication, to increase or decrease a volume output, and so forth. Itshould be understood that the illustrated input structures 14 are merelyexemplary, and that the handheld device 60 may include any number ofsuitable user input structures existing in various forms includingbuttons, switches, keys, knobs, scroll wheels, and so forth.

As shown in FIG. 5, the handheld device 60 may include various I/O ports12. For instance, the depicted I/O ports 12 may include a proprietaryconnection port 12 a for transmitting and receiving data files or forcharging a power source 26 and an audio connection port 12 b forconnecting the device 60 to an audio output device (e.g., headphones orspeakers). Further, in embodiments where the handheld device 60 providesmobile phone functionality, the device 60 may include an I/O port 12 cfor receiving a subscriber identify module (SIM) card (e.g., anexpansion card 22).

The display device 28, which may be an LCD, OLED, or any suitable typeof display, may display various images generated by the handheld device60. For example, the display 28 may display various system indicators 64providing feedback to a user with regard to one or more states ofhandheld device 60, such as power status, signal strength, externaldevice connections, and so forth. The display may also display a GUI 52that allows a user to interact with the device 60, as discussed abovewith reference to FIG. 4. The GUI 52 may include graphical elements,such as the icons 54 which may correspond to various applications thatmay be opened or executed upon detecting a user selection of arespective icon 54. By way of example, one of the icons 54 may representa camera application 66 that may be used in conjunction with a camera 30(shown in phantom lines in FIG. 5) for acquiring images. Referringbriefly to FIG. 6, a rear view of the handheld electronic device 60depicted in FIG. 5 is illustrated, which shows the camera 30 as beingintegrated with the housing 42 and positioned on the rear of thehandheld device 60.

As mentioned above, image data acquired using the camera 30 may beprocessed using the image processing circuitry 32, which my includehardware (e.g., disposed within the enclosure 42) and/or software storedon one or more storage devices (e.g., memory 18 or non-volatile storage20) of the device 60. Images acquired using the camera application 66and the camera 30 may be stored on the device 60 (e.g., in storagedevice 20) and may be viewed at a later time using a photo viewingapplication 68.

The handheld device 60 may also include various audio input and outputelements. For example, the audio input/output elements, depictedgenerally by reference numeral 70, may include an input receiver, suchas one or more microphones. For instance, where the handheld device 60includes cell phone functionality, the input receivers may be configuredto receive user audio input, such as a user's voice. Additionally, theaudio input/output elements 70 may include one or more outputtransmitters. Such output transmitters may include one or more speakerswhich may function to transmit audio signals to a user, such as duringthe playback of music data using a media player application 72. Further,in embodiments where the handheld device 60 includes a cell phoneapplication, an additional audio output transmitter 74 may be provided,as shown in FIG. 5. Like the output transmitters of the audioinput/output elements 70, the output transmitter 74 may also include oneor more speakers configured to transmit audio signals to a user, such asvoice data received during a telephone call. Thus, the audioinput/output elements 70 and 74 may operate in conjunction to functionas the audio receiving and transmitting elements of a telephone.

Having now provided some context with regard to various forms that theelectronic device 10 may take, the present discussion will now focus onthe image processing circuitry 32 depicted in FIG. 1. As mentionedabove, the image processing circuitry 32 may be implemented usinghardware and/or software components, and may include various processingunits that define an image signal processing (ISP) pipeline. Inparticular, the following discussion may focus on aspects of the imageprocessing techniques set forth in the present disclosure, particularlythose relating to defective pixel detection/correction techniques, lensshading correction techniques, demosaicing techniques, and imagesharpening techniques.

Referring now to FIG. 7, a simplified top-level block diagram depictingseveral functional components that may be implemented as part of theimage processing circuitry 32 is illustrated, in accordance with oneembodiment of the presently disclosed techniques. Particularly, FIG. 7is intended to illustrate how image data may flow through the imageprocessing circuitry 32, in accordance with at least one embodiment. Inorder to provide a general overview of the image processing circuitry32, a general description of how these functional components operate toprocess image data is provided here with reference to FIG. 7, while amore specific description of each of the illustrated functionalcomponents, as well as their respective sub-components, will be furtherprovided below.

Referring to the illustrated embodiment, the image processing circuitry32 may include image signal processing (ISP) front-end processing logic80, ISP pipe processing logic 82, and control logic 84. Image datacaptured by the imaging device 30 may first be processed by the ISPfront-end logic 80 and analyzed to capture image statistics that may beused to determine one or more control parameters for the ISP pipe logic82 and/or the imaging device 30. The ISP front-end logic 80 may beconfigured to capture image data from an image sensor input signal. Forinstance, as shown in FIG. 7, the imaging device 30 may include a camerahaving one or more lenses 88 and image sensor(s) 90. As discussed above,the image sensor(s) 90 may include a color filter array (e.g., a Bayerfilter) and may thus provide both light intensity and wavelengthinformation captured by each imaging pixel of the image sensors 90 toprovide for a set of raw image data that may be processed by the ISPfront-end logic 80. For instance, the output 92 from the imaging device30 may be received by a sensor interface 94, which may then provide theraw image data 96 to the ISP front-end logic 80 based, for example, onthe sensor interface type. By way of example, the sensor interface 94may utilize a Standard Mobile Imaging Architecture (SMIA) interface orother serial or parallel camera interfaces, or some combination thereof.In certain embodiments, the ISP front-end logic 80 may operate withinits own clock domain and may provide an asynchronous interface to thesensor interface 94 to support image sensors of different sizes andtiming requirements. The sensor interface 94 may include, in someembodiments, a sub-interface on the sensor side (e.g., sensor-sideinterface) and a sub-interface on the ISP front-end side, with thesub-interfaces forming the sensor interface 94.

The raw image data 96 may be provided to the ISP front-end logic 80 andprocessed on a pixel-by-pixel basis in a number of formats. Forinstance, each image pixel may have a bit-depth of 8, 10, 12, or 14bits. Various examples of memory formats showing how pixel data may bestored and addressed in memory are discussed in further detail below.The ISP front-end logic 80 may perform one or more image processingoperations on the raw image data 96, as well as collect statistics aboutthe image data 96. The image processing operations, as well as thecollection of statistical data, may be performed at the same or atdifferent bit-depth precisions. For example, in one embodiment,processing of the raw image pixel data 96 may be performed at aprecision of 14-bits. In such embodiments, raw pixel data received bythe ISP front-end logic 80 that has a bit-depth of less than 14 bits(e.g., 8-bit, 10-bit, 12-bit) may be up-sampled to 14-bits for imageprocessing purposes. In another embodiment, statistical processing mayoccur at a precision of 8-bits and, thus, raw pixel data having a higherbit-depth may be down-sampled to an 8-bit format for statisticspurposes. As will be appreciated, down-sampling to 8-bits may reducehardware size (e.g., area) and also reduce processing/computationalcomplexity for the statistics data. Additionally, the raw image data maybe averaged spatially to allow for the statistics data to be more robustto noise.

Further, as shown in FIG. 7, the ISP front-end logic 80 may also receivepixel data from the memory 108. For instance, as shown by referencenumber 98, the raw pixel data may be sent to the memory 108 from thesensor interface 94. The raw pixel data residing in the memory 108 maythen be provided to the ISP front-end logic 80 for processing, asindicated by reference number 100. The memory 108 may be part of thememory device 18, the storage device 20, or may be a separate dedicatedmemory within the electronic device 10 and may include direct memoryaccess (DMA) features. Further, in certain embodiments, the ISPfront-end logic 80 may operate within its own clock domain and providean asynchronous interface to the sensor interface 94 to support sensorsof different sizes and having different timing requirements.

Upon receiving the raw image data 96 (from sensor interface 94) or 100(from memory 108), the ISP front-end logic 80 may perform one or moreimage processing operations, such as temporal filtering and/or binningcompensation filtering. The processed image data may then be provided tothe ISP pipe logic 82 (output signal 109) for additional processingprior to being displayed (e.g., on display device 28), or may be sent tothe memory (output signal 110). The ISP pipe logic 82 receives the“front-end” processed data, either directly form the ISP front-end logic80 or from the memory 108 (input signal 112), and may provide foradditional processing of the image data in the raw domain, as well as inthe RGB and YCbCr color spaces. Image data processed by the ISP pipelogic 82 may then be output (signal 114) to the display 28 for viewingby a user and/or may be further processed by a graphics engine or GPU.Additionally, output from the ISP pipe logic 82 may be sent to memory108 (signal 115) and the display 28 may read the image data from memory108 (signal 116), which may, in certain embodiments, be configured toimplement one or more frame buffers. Further, in some implementations,the output of the ISP pipe logic 82 may also be provided to acompression/decompression engine 118 (signal 117) for encoding/decodingthe image data. The encoded image data may be stored and then laterdecompressed prior to being displayed on the display 28 device (signal119). By way of example, the compression engine or “encoder” 118 may bea JPEG compression engine for encoding still images, or an H.264compression engine for encoding video images, or some combinationthereof, as well as a corresponding decompression engine for decodingthe image data. Additional information with regard to image processingoperations that may be provided in the ISP pipe logic 82 will bediscussed in greater detail below with regard to FIGS. 98 to 133. Also,it should be noted that the ISP pipe logic 82 may also receive raw imagedata from the memory 108, as depicted by input signal 112.

Statistical data 102 determined by the ISP front-end logic 80 may beprovided to a control logic unit 84. The statistical data 102 mayinclude, for example, image sensor statistics relating to auto-exposure,auto-white balance, auto-focus, flicker detection, black levelcompensation (BLC), lens shading correction, and so forth. The controllogic 84 may include a processor and/or microcontroller configured toexecute one or more routines (e.g., firmware) that may be configured todetermine, based upon the received statistical data 102, controlparameters 104 for the imaging device 30, as well as control parameters106 for the ISP pipe processing logic 82. By way of example only, thecontrol parameters 104 may include sensor control parameters (e.g.,gains, integration time for exposure control), camera flash controlparameters, lens control parameters (e.g., focal length for focusing orzoom), or a combination of such parameters. The ISP control parameters106 may include gain levels and color correction matrix (CCM)coefficients for auto-white balance and color adjustment (e.g., duringRGB processing), as well as lens shading correction parameters which, asdiscussed below, may be determined based upon white point balanceparameters. In some embodiments, the control logic 84 may, in additionto analyzing statistics data 102, also analyze historical statistics,which may be stored on the electronic device 10 (e.g., in memory 18 orstorage 20).

Referring to the illustrated embodiment, the image processing circuitry32 may include image signal processing (ISP) front-end processing logic80, ISP pipe processing logic 82, and control logic 84. Image datacaptured by the imaging device 30 may first be processed by the ISPfront-end logic 80 and analyzed to capture image statistics that may beused to determine one or more control parameters for the ISP pipe logic82 and/or the imaging device 30. The ISP front-end logic 80 may beconfigured to capture image data from an image sensor input signal. Forinstance, as shown in FIG. 7, the imaging device 30 may include a camerahaving one or more lenses 88 and image sensor(s) 90. As discussed above,the image sensor(s) 90 may include a color filter array (e.g., a Bayerfilter) and may thus provide both light intensity and wavelengthinformation captured by each imaging pixel of the image sensors 90 toprovide for a set of raw image data that may be processed by the ISPfront-end logic 80. For instance, the output 92 from the imaging device30 may be received by a sensor interface 94, which may then provide theraw image data 96 to the ISP front-end logic 80 based, for example, onthe sensor interface type. By way of example, the sensor interface 94may utilize a Standard Mobile Imaging Architecture (SMIA) interface orother serial or parallel camera interfaces, or some combination thereof.In certain embodiments, the ISP front-end logic 80 may operate withinits own clock domain and may provide an asynchronous interface to thesensor interface 94 to support image sensors of different sizes andtiming requirements.

FIG. 8 shows a block diagram depicting another embodiment of the imageprocessing circuitry 32, wherein the same components are labeled withthe same reference numbers. Generally, the operation and functionalityof the image processing circuitry 32 of FIG. 8 is similar to the imageprocessing circuitry 32 of FIG. 7, except that the embodiment shown inFIG. 8 further includes an ISP back-end processing logic unit 120, whichmay be coupled downstream from the ISP pipeline 82 and may provide foradditional post-processing steps.

In the illustrated embodiment, the ISP back-end logic 120 may receivethe output 114 from the ISP pipeline 82 and perform post-processing thereceived data 114. Additionally, the ISP back-end 120 may receive imagedata directly from memory 108, as shown by input 124. As will bediscussed further below with reference to FIGS. 134 to 142, oneembodiment of the ISP-back-end logic 120 may provide for dynamic rangecompression of image data (often referred to as “tone mapping”),brightness, contrast, and color adjustments, as well as scaling logicfor scaling the image data to a desired size or resolution (e.g., basedupon a resolution of an output display device). Further, theISP-back-end logic 120 may also include feature detection logic fordetecting certain features in the image data. For instance, in oneembodiment, the feature detection logic may include face detection logicconfigured to identify areas in which faces and/or facial features arelocated and/or positioned within the image data. Facial detection datamay be fed to the front-end statistics processing unit as feedback datafor determination auto-white balance, auto-focus, flicker, andauto-exposure statistics. For instance, the statistics processing unitsin the ISP front-end 80 (discussed in more detail below in FIGS. 68-97)may be configured to select windows for statistics processing based onthe determined locations of faces and/or facial features in the imagedata.

In some embodiments, the facial detection data, in addition to orinstead of being fed back to an ISP front-end statistics feedbackcontrol loop, may also be provided to at least one of local tone mappingprocessing logic, an ISP back-end statistics unit, or to theencoder/decoder unit 118. As discussed further below, the facialdetection data provided to the back-end statistics unit may be utilizedto control quantization parameters. For instance, when encoding orcompressing the output image data (e.g., in macroblocks) quantizationmay be reduced for areas of the image that have been determined toinclude faces and/or facial features, thus improving the visual qualityof faces and facial features when the image is displayed and viewed by auser.

In further embodiments, the feature detection logic may also beconfigured to detect the locations of corners of objects in the imageframe. This data may be used to identify the location of features inconsecutive image frames in order to determine an estimation of globalmotion between frames, which may be used to perform certain imageprocessing operations, such as image registration. In one embodiment,the identification of corner features and the like may be particularlyuseful for algorithms that combine multiple image frames, such as incertain high dynamic range (HDR) imaging algorithms, as well as certainpanoramic stitching algorithms.

Further, as shown in FIG. 8, image data processed by the ISP back-endlogic 120 may be output (signal 126) to the display device 28 forviewing by a user and/or may be further processed by a graphics engineor GPU. Additionally, output from the ISP back-end logic 120 may be sentto memory 108 (signal 122) and the display 28 may read the image datafrom memory 108 (signal 116), which may, in certain embodiments, beconfigured to implement one or more frame buffers. In the illustratedembodiment, the output of the ISP back-end logic 120 may also beprovided to the compression/decompression engine 118 (signal 117) forencoding/decoding the image data for storage and subsequent playback, asgenerally discussed above in FIG. 7. In further embodiments, the ISPsub-system 32 of FIG. 8 may have the option of bypassing the ISPback-end processing unit 120. In such embodiments, if the back-endprocessing unit 120 is bypassed, the ISP sub-system 32 of FIG. 8 mayoperate in a manner similar to that shown in FIG. 7, i.e., the output ofthe ISP pipeline 82 is sent directly/indirectly one or more of memory108, the encoder/decoder 118, or the display 28.

The image processing techniques depicted in the embodiments shown inFIG. 7 and FIG. 8 may be generally summarized by the method 130 depictedby way of a flow chart in FIG. 9. As shown, the method 130 begins atblock 132, at which raw image data (e.g., Bayer pattern data) isreceived using a sensor interface from an image sensor (e.g., 90). Atblock 134, the raw image data received at step 132 is processed usingthe ISP front-end logic 80. As mentioned above, the ISP front-end logic80 may be configured to apply temporal filtering, binning compensationfiltering. Next at step 136, the raw image data processed by the ISPfront-end logic 80 may be further processed by the ISP pipeline 82,which may perform various processing steps to demosaic the raw imagedata into full-color RGB data and to further convert the RGB color datainto a YUV or YC1C2 color space (where C1 and C2 represent differentchroma difference colors and wherein C1 and C2 may representblue-difference (Cb) and red-difference (Cr) chroma in one embodiment).

From step 136, the method 130 may either continue to step 138 or to step160. For instance, in an embodiment (FIG. 7) where the output of the ISPpipeline 82 is provided to a display device 28, the method 130 continuesto step 140, wherein the YC1C2 image data is displayed using the displaydevice 28 (or sent to from the ISP pipeline 82 to memory 108).Alternatively, in an embodiment where the output of the ISP pipeline 82is post-processed by an ISP back-end unit 120 (FIG. 8), the method 130may continue from step 136 to step 138, where the YC1C2 output of theISP pipeline 82 is processed using the ISP back-end processing logic 120before being displayed by the display device at step 140.

Due to the generally complex design of the image processing circuitry 32shown herein, it may be beneficial to separate the discussion of the ISPfront-end logic 80, the ISP pipe processing logic 82 (or ISP pipeline),and the ISP back-end processing logic 120 into separate sections, asshown below. Particularly, FIGS. 10 to 97 of the present application mayrelate to the discussion of various embodiments and aspects of the ISPfront-end logic 80, FIGS. 98 to 133 of the present application mayrelate to the discussion of various embodiments and aspects of the ISPpipe processing logic 82, and FIGS. 134 to 142 may relate to discussionof various embodiments and aspects of the ISP back-end logic 120.

The ISP Front-End Processing Logic

FIG. 10 is a more detailed block diagram showing functional logic blocksthat may be implemented in the ISP front-end logic 80, in accordancewith one embodiment. Depending on the configuration of the imagingdevice 30 and/or sensor interface 94, as discussed above in FIG. 7, rawimage data may be provided to the ISP front-end logic 80 by one or moreimage sensors 90. In the depicted embodiment, raw image data may beprovided to the ISP front-end logic 80 by a first image sensor 90 a(Sensor0) and a second image sensor 90 b (Sensor1). As will be discussedfurther below, each image sensor 90 a and 90 b may be configured toapply binning to full resolution image data in order to increasesignal-to-noise ratio of the image signal. For instance, a binningtechnique, such as 2×2 binning, may be applied which may interpolate a“binned” raw image pixel based upon four full-resolution image pixels ofthe same color. In one embodiment, this may result in there being fouraccumulated signal components associated with the binned pixel versus asingle noise component, thus improving signal-to-noise of the imagedata, but reducing overall resolution. Additionally, binning may alsoresult in an uneven or non-uniform spatial sampling of the image data,which may be corrected using binning compensation filtering, as will bediscussed in more detail below.

As shown, the image sensors 90 a and 90 b may provide the raw image dataas signals Sif0 and Sif1, respectively. Each of the image sensors 90 aand 90 b may be generally associated with the respective statisticsprocessing units 142 (StatsPipe0) and 144 (StatsPipe1), which may beconfigured to process image data for the determination of one or moresets of statistics (as indicated by signals Stats0 and Stats1),including statistics relating to auto-exposure, auto-white balance,auto-focus, flicker detection, black level compensation, and lensshading correction, and so forth. In certain embodiments, when only oneof the sensors 90 a or 90 b is actively acquiring image, the image datamay be sent to both StatsPipe0 and StatsPipe1 if additional statisticsare desired. For instance, to provide one example, if StatsPipe0 andStatsPipe1 are both available, StatsPipe0 may be utilized to collectstatistics for one color space (e.g., RGB), and StatsPipe1 may beutilized to collect statistics for another color space (e.g., YUV orYCbCr). That is, the statistics process units 142 and 144 may operate inparallel to collect multiple sets of statistics for each frame of theimage data acquired by the active sensor.

In the present embodiment, five asynchronous sources of data areprovided in the ISP front-end 80. These include: (1) a direct input froma sensor interface corresponding to Sensor0 (90 a) (referred to as Sif0or Sens0), (2) a direct input from a sensor interface corresponding toSensor1 (90 b) (referred to as Sif1 or Sens1), (3) Sensor0 data inputfrom the memory 108 (referred to as SifIn0 or Sens0DMA), which mayinclude a DMA interface, (4) Sensor1 data input from the memory 108(referred to as SifIn1 or Sens1DMA), and (5) a set of image data withframes from Sensor0 and Sensor1 data input retrieved from the memory 108(referred to as FeProcIn or ProcInDMA). The ISP front-end 80 may alsoinclude multiple destinations to which image data from the sources maybe routed, wherein each destination may be either a storage location inmemory (e.g., in 108), or a processing unit. For instance, in thepresent embodiment, the ISP front-end 80 includes six destinations: (1)Sif0DMA for receiving Sensor0 data in the memory 108, (2) Sif1DMA forreceiving Sensor1 data in the memory 108, (3) the first statisticsprocessing unit 142 (StatsPipe0), (4) the second statistics processingunit 144 (StatsPipe1), (5) the front-end pixel processing unit (FEProc)150, and (6) FeOut (or FEProcOut) to memory 108 or the ISP pipeline 82(discussed in further detail below). In one embodiment, the ISPfront-end 80 may be configured such that only certain destinations arevalid for a particular source, as shown in Table 1 below.

TABLE 1 Example of ISP Front-end valid destinations for each source SIf0SIf1 StatsPipe StatsPipe DMA DMA 0 1 FEProc FEOut Sens0 X X X X X Sens1X X X X X Sens0DMA X Sens1DMA X ProcInDMA X X

For instance, in accordance with Table 1, source Sens0 (sensor interfaceof Sensor0) may be configured to provide data to destinations SHODMA(signal 154), StatsPipe0 (signal 156), StatsPipe1 (signal 158), FEProc(signal 160), or FEOut (signal 162). With regard to FEOut, source datamay, in some instances, be provided to FEOut to bypass pixel processingby FEProc, such as for debugging or test purposes. Additionally, sourceSens1 (sensor interface of Sensor1) may be configured to provide data todestinations SIf1DMA (signal 164), StatsPipe0 (signal 166), StatsPipe1(signal 168), FEProc (signal 170), or FEOut (signal 172), sourceSens0DMA (Sensor0 data from memory 108) may be configured to providedata to StatsPipe0 (signal 174), source Sens1DMA (Sensor1 data frommemory 108) may be configured to provide data to StatsPipe1 (signal176), and source ProcInDMA (Sensor0 and Sensor1 data from memory 108)may be configured to provide data to FEProc (signal 178) and FEOut(signal 182).

It should be noted that the presently illustrated embodiment isconfigured such that Sens0DMA (Sensor0 frames from memory 108) andSens1DMA (Sensor1 frames from memory 108) are only provided toStatsPipe0 and StatesPipe1, respectively. This configuration allows theISP front-end 80 to retain a certain number of previous frames (e.g., 5frames) in memory. For example, due to a delay or lag between the time auser initiates a capture event (e.g., transitioning the image systemfrom a preview mode to a capture or a recording mode, or even by justturning on or initializing the image sensor) using the image sensor towhen an image scene is captured, not every frame that the user intendedto capture may be captured and processed in substantially real-time.Thus, by retaining a certain number of previous frames in memory 108(e.g., from a preview phase), these previous frames may be processedlater or alongside the frames actually captured in response to thecapture event, thus compensating for any such lag and providing a morecomplete set of image data.

With regard to the illustrated configuration of FIG. 10, it should benoted that the StatsPipe0 142 is configured to receive one of the inputs156 (from Sens0), 166 (from Sens1), and 174 (from Sens0DMA), asdetermined by a selection logic 146, such as a multiplexer. Similarly,selection logic 148 may select an input from the signals 158, 176, and168 to provide to StatsPipe1, and selection logic 152 may select aninput from the signals 160, 170, and 178 to provide to FEProc. Asmentioned above, the statistical data (Stats0 and Stats1) may beprovided to the control logic 84 for the determination of variouscontrol parameters that may be used to operate the imaging device 30and/or the ISP pipe processing logic 82. As can be appreciated, theselection logic blocks (146, 148, and 152) shown in FIG. 10 may beprovided by any suitable type of logic, such as a multiplexer thatselects one of multiple input signals in response to a control signal.

The pixel processing unit (FEProc) 150 may be configured to performvarious image processing operations on the raw image data on apixel-by-pixel basis. As shown, FEProc 150, as a destination processingunit, may receive image data from sources Sens0 (signal 160), Sens1(signal 170), or ProcInDMA (signal 178) by way of the selection logic152. FEProc 150 may also receive and output various signals (e.g., Rin,Hin, Hout, and Yout—which may represent motion history and luma dataused during temporal filtering) when performing the pixel processingoperations, which may include temporal filtering and binningcompensation filtering, as will be discussed further below. The output109 (FEProcOut) of the pixel processing unit 150 may then be forwardedto the ISP pipe logic 82, such as via one or more first-in-first-out(FIFO) queues, or may be sent to the memory 108.

Further, as shown in FIG. 10, the selection logic 152, in addition toreceiving the signals 160, 170, and 178, may further receive the signals180 and 184. The signal 180 may represented “pre-processed” raw imagedata from StatsPipe0, and the signal 184 may represent “pre-processed”raw image data from StatsPipe1. As will be discussed below, each of thestatistics processing units may apply one or more pre-processingoperations to the raw image data before collecting statistics. In oneembodiment, each of the statistics processing units may perform a degreeof defective pixel detection/correction, lens shading correction, blacklevel compensation, and inverse black level compensation. Thus, thesignals 180 and 184 may represent raw image data that has been processedusing the aforementioned pre-processing operations (as will be discussedin further detail below in FIG. 68). Thus, the selection logic 152 givesthe ISP front-end processing logic 80 the flexibility of providingeither un-pre-processed raw image data from the Sensor0 (signal 160) andSensor1 (signal 170) or pre-processed raw image data from StatsPipe0(signal 180) and StatsPipe1 (signal 184). Additionally, as shown byselection logic units 186 and 188, the ISP front-end processing logic 80also has the flexibility of writing either un-pre-processed raw imagedata from Sensor0 (signal 154) or Sensor1 (signal 164) to the memory108, or writing pre-processed raw image data from StatsPipe0 (signal180) or StatsPipe1 (signal 184) to the memory 108.

To control the operation of the ISP front-end logic 80, a front-endcontrol unit 190 is provided. The control unit 190 may be configured toinitialize and program control registers (referred to herein as “goregisters”) for configuring and starting the processing of an imageframe and to select an appropriate register bank(s) for updatingdouble-buffered data registers. In some embodiments, the control unit190 may also provide performance monitoring logic to log clock cycles,memory latency, and quality of service (QOS) information. Further, thecontrol unit 190 may also control dynamic clock gating, which may beused to disable clocks to one or more portions of the ISP front-end 0when there is not enough data in the input queue from an active sensor.

Using the “go registers” mentioned above, the control unit 190 may beable to control the updating of various parameters for each of theprocessing units (e.g., StatsPipe0, StatsPipe1, and FEProc) and mayinterface with the sensor interfaces to control the starting andstopping of the processing units. Generally each of the front-endprocessing units operates on a frame-by-frame basis. As discussed above(Table 1), the input to the processing units may be from the sensorinterface (Sens0 or Sens1) or from memory 108. Further, the processingunits may utilize various parameters and configuration data, which maybe stored in corresponding data registers. In one embodiment, the dataregisters associated with each processing unit or destination may begrouped into blocks forming a register bank group. In the embodiment ofFIG. 10, seven register bank groups may be defined in ISP Front-end:SIf0, SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut and ProcIn. Eachregister block address space is duplicated to provide two banks ofregisters. Only the registers that are double buffered are instantiatedin the second bank. If a register is not double buffered, the address inthe second bank may be mapped to the address of the same register in thefirst bank.

For registers that are double buffered, registers from one bank areactive and used by the processing units while the registers from theother bank are shadowed. The shadowed register may be updated by thecontrol unit 190 during the current frame interval while hardware isusing the active registers. The determination of which bank to use for aparticular processing unit at a particular frame may be specified by a“NextBk” (next bank) field in a go register corresponding to the sourceproviding the image data to the processing unit. Essentially, NextBk isa field that allows the control unit 190 to control which register bankbecomes active on a triggering event for the subsequent frame.

Before discussing the operation of the go registers in detail, FIG. 11provides a general method 200 for processing image data on aframe-by-frame basis in accordance with the present techniques.Beginning at step 202, the destination processing units targeted by adata source (e.g., Sens0, Sens1, Sens0DMA, Sens1DMA, or ProcInDMA) enteran idle state. This may indicate that processing for the current frameis completed and, therefore, the control unit 190 may prepare forprocessing the next frame. For instance, at step 204, programmableparameters for each destination processing unit are updated. This mayinclude, for example, updating the NextBk field in the go registercorresponding to the source, as well as updating any parameters in thedata registers corresponding to the destination units. Thereafter, atstep 206, a triggering event may place the destination units into a runstate. Further, as shown at step 208, each destination unit targeted bythe source completes its processing operations for the current frame,and the method 200 may subsequently return to step 202 for theprocessing of the next frame.

FIG. 12 depicts a block diagram view showing two banks of data registers210 and 212 that may be used by the various destination units of theISP-front end. For instance, Bank 0 (210) may include the data registers1-n (210 a-210 d), and Bank 1 (212) may include the data registers 1-n(212 a-212 d). As discussed above, the embodiment shown in FIG. 10 mayutilize a register bank (Bank 0) having seven register bank groups(e.g., SIf0, SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut and ProcIn).Thus, in such an embodiment, the register block address space of eachregister is duplicated to provide a second register bank (Bank 1).

FIG. 12 also illustrates go register 214 that may correspond to one ofthe sources. As shown, the go register 214 includes a “NextVld” field216 and the above-mentioned “NextBk” field 218. These fields may beprogrammed prior to starting the processing of the current frame.Particularly, NextVld may indicate the destination(s) to where data fromthe source is to be sent. As discussed above, NextBk may select acorresponding data register from either Bank0 or Bank1 for eachdestination targeted, as indicated by NextVld. Though not shown in FIG.12, the go register 214 may also include an arming bit, referred toherein as a “go bit,” which may be set to arm the go register. When atriggering event 226 for a current frame is detected, NextVld and NextBkmay be copied into a CurrVld field 222 and a CurrBk field 224 of acorresponding current or “active” register 220. In one embodiment, thecurrent register(s) 220 may be read-only registers that may set byhardware, while remaining inaccessible to software commands within theISP front-end 80.

As will be appreciated, for each ISP front-end source, a correspondinggo register may be provided. For the purposes of this disclosure, the goregisters corresponding to the above-discussed sources Sens0, Sens1,Sens0DMA, Sens1DMA, and ProcInDMA may be referred to as Sens0Go,Sens1Go, Sens0DMAGo, Sens1DMAGo and ProcInDMAGo, respectively. Asmentioned above, the control unit may utilize the go registers tocontrol the sequencing of frame processing within the ISP front end 80.Each go register contains a NextVld field and a NextBk field to indicatewhat destinations will be valid, and which register bank (0 or 1) willbe used, respectively, for the next frame. When the next frame'striggering event 226 occurs, the NextVld and NextBk fields are copied toa corresponding active read-only register 220 that indicates the currentvalid destinations and bank numbers, as shown above in FIG. 12. Eachsource may be configured to operate asynchronously and can send data toany of its valid destinations. Further, it should be understood that foreach destination, generally only one source may be active during acurrent frame.

With regard to the arming and triggering of the go register 214,asserting an arming bit or “go bit” in the go register 214 arms thecorresponding source with the associated NextVld and NextBk fields. Fortriggering, various modes are available depending on whether the sourceinput data is read from memory (e.g., Sens0DMA, Sens1DMA or ProcInDMA),or whether the source input data is from a sensor interface (e.g., Sens0or Sens1). For instance, if the input is from memory 108, the arming ofthe go bit itself may serve as the triggering event, since the controlunit 190 has control over when data is read from the memory 108. If theimage frames are being input by the sensor interface, then triggeringevent may depend on the timing at which the corresponding go register isarmed relative to when data from the sensor interface is received. Inaccordance with the present embodiment, three different techniques fortriggering timing from a sensor interface input are shown in FIGS.13-15.

Referring first to FIG. 13, a first scenario is illustrated in whichtriggering occurs once all destinations targeted by the sourcetransition from a busy or run state to an idle state. Here, a datasignal VVALID (228) represents an image data signal from a source. Thepulse 230 represents a current frame of image data, the pulse 236represents the next frame of image data, and the interval 232 representsa vertical blanking interval (VBLANK) 232 (e.g., represents the timedifferential between the last line of the current frame 230 and the nextframe 236). The time differential between the rising edge and fallingedge of the pulse 230 represents a frame interval 234. Thus, in FIG. 13,the source may be configured to trigger when all targeted destinationshave finished processing operations on the current frame 230 andtransition to an idle state. In this scenario, the source is armed(e.g., by setting the arming or “go” bit) before the destinationscomplete processing so that the source can trigger and initiateprocessing of the next frame 236 as soon as the targeted destinations goidle. During the vertical blanking interval 232 the processing units maybe set up and configured for the next frame 236 using the register banksspecified by the go register corresponding to the source before thesensor input data arrives. By way of example only, read buffers used byFEProc 150 may be filled before the next frame 236 arrives. In thiscase, shadowed registers corresponding to the active register banks maybe updated after the triggering event, thus allowing for a full frameinterval to setup the double-buffered registers for the next frame(e.g., after frame 236).

FIG. 14 illustrates a second scenario in which the source is triggeredby arming the go bit in the go register corresponding to the source.Under this “trigger-on-go” configuration, the destination units targetedby the source are already idle and the arming of the go bit is thetriggering event. This triggering mode may be utilized for registersthat are not double-buffered and, therefore, are updated during verticalblanking (e.g., as opposed to updating a double-buffered shadow registerduring the frame interval 234).

FIG. 15 illustrates a third triggering mode in which the source istriggered upon detecting the start of the next frame, i.e., a risingVSYNC. However, it should be noted that in this mode, if the go registeris armed (by setting the go bit) after the next frame 236 has alreadystarted processing, the source will use the target destinations andregister banks corresponding to the previous frame, since the CurrVldand CurrBk fields are not updated before the destination startprocessing. This leaves no vertical blanking interval for setting up thedestination processing units and may potentially result in droppedframes, particularly when operating in a dual sensor mode. It should benoted, however, that this mode may nonetheless result in accurateoperation if the image processing circuitry 32 is operating in a singlesensor mode that uses the same register banks for each frame (e.g., thedestination (NextVld) and register banks (NextBk) do not change).

Referring now to FIG. 16, a control register (or “go register”) 214 isillustrated in more detail. The go register 214 includes the arming “go”bit 238, as well as the NextVld field 216 and the NextBk field 218. Asdiscussed above, each source (e.g., Sens0, Sens1, Sens0DMA, Sens1DMA, orProcInDMA) of the ISP front-end 80 may have a corresponding go register214. In one embodiment, the go bit 238 may be a single-bit field, andthe go register 214 may be armed by setting the go bit 238 to 1. TheNextVld field 216 may contain a number of bits corresponding to thenumber of destinations in the ISP front-end 80. For instance, in theembodiment shown in FIG. 10, the ISP front-end includes sixdestinations: Sif0DMA, Sif1DMA, StatsPipe0, StatsPipe1, FEProc, andFEOut. Thus, the go register 214 may include six bits in the NextVldfield 216, with one bit corresponding to each destination, and whereintargeted destinations are set to 1. Similarly, the NextBk field 216 maycontain a number of bits corresponding to the number of data registersin the ISP front-end 80. For instance, as discussed above, theembodiment of the ISP front-end 80 shown in FIG. 10 may include sevendata registers: SIf0, SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut andProcIn. Accordingly, the NextBk field 218 may include seven bits, withone bit corresponding to each data register, and wherein data registerscorresponding to Bank 0 and 1 are selected by setting their respectivebit values to 0 or 1, respectively. Thus, using the go register 214, thesource, upon triggering, knows precisely which destination units are toreceive frame data, and which register banks are to be used forconfiguring the targeted destination units.

Additionally, due to the dual sensor configuration supported by the ISPcircuitry 32, the ISP front-end may operate in a single sensorconfiguration mode (e.g., only one sensor is acquiring data) and a dualsensor configuration mode (e.g., both sensors are acquiring data). In atypical single sensor configuration, input data from a sensor interface,such as Sens0, is sent to StatsPipe0 (for statistics processing) andFEProc (for pixel processing). In addition, sensor frames may also besent to memory (SIf0DMA) for future processing, as discussed above.

An example of how the NextVld fields corresponding to each source of theISP front-end 80 may be configured when operating in a single sensormode is depicted below in Table 2.

TABLE 2 NextVld per source example: Single sensor mode SIf0 SIf1StatsPipe StatsPipe DMA DMA 0 1 FEProc FEOut Sens0Go 1 X 1 0 1 0 Sens1GoX 0 0 0 0 0 Sens0DMAGo X X 0 X X X Sens1DMAGo X X X 0 X X ProcInDMAGo XX X X 0 0As discussed above with reference to Table 1, the ISP front-end 80 maybe configured such that only certain destinations are valid for aparticular source. Thus, the destinations in Table 2 marked with “X” areintended to indicate that the ISP front-end 80 is not configured toallow a particular source to send frame data to that destination. Forsuch destinations, the bits of the NextVld field of the particularsource corresponding to that destination may always be 0. It should beunderstood, however, that this is merely one embodiment and, indeed, inother embodiments, the ISP front-end 80 may be configured such that eachsource is capable of targeting each available destination unit.

The configuration shown above in Table 2 represents a single sensor modein which only Sensor0 is providing frame data. For instance, the Sens0Goregister indicates destinations as being SIf0DMA, StatsPipe0, andFEProc. Thus, when triggered, each frame of the Sensor0 image data, issent to these three destinations. As discussed above, SIf0DMA may storeframes in memory 108 for later processing, StatsPipe0 applies statisticsprocessing to determine various statistic data points, and FEProcprocesses the frame using, for example, temporal filtering and binningcompensation filtering. Further, in some configurations where additionalstatistics are desired (e.g., statistics in different color spaces),StatsPipe1 may also be enabled (corresponding NextVld set to 1) duringthe single sensor mode. In such embodiments, the Sensor0 frame data issent to both StatsPipe0 and StatsPipe1. Further, as shown in the presentembodiment, only a single sensor interface (e.g., Sens0 or alternativelySen0) is the only active source during the single sensor mode.

With this in mind, FIG. 17 provides a flow chart depicting a method 240for processing frame data in the ISP front-end 80 when only a singlesensor is active (e.g., Sensor 0). While the method 240 illustrates inparticular the processing of Sensor0 frame data by FEProc 150 as anexample, it should be understood that this process may be applied to anyother source and corresponding destination unit in the ISP front-end 80.Beginning at step 242, Sensor0 begins acquiring image data and sendingthe captured frames to the ISP front-end 80. The control unit 190 mayinitialize programming of the go register corresponding to Sens0 (theSensor0 interface) to determine target destinations (including FEProc)and what bank registers to use, as shown at step 244. Thereafter,decision logic 246 determines whether a source triggering event hasoccurred. As discussed above, frame data input from a sensor interfacemay utilize different triggering modes (FIGS. 13-15). If a trigger eventis not detected, the process 240 continues to wait for the trigger. Oncetriggering occurs, the next frame becomes the current frame and is sentto FEProc (and other target destinations) for processing at step 248.FEProc may be configured using data parameters based on a correspondingdata register (ProcPipe) specified in the NextBk field of the Sens0Goregister. After processing of the current frame is completed at step250, the method 240 may return to step 244, at which the Sens0Goregister is programmed for the next frame.

When both Sensor0 and Sensor1 of the ISP front-end 80 are both active,statistics processing remains generally straightforward, since eachsensor input may be processed by a respective statistics block,StatsPipe0 and StatsPipe1. However, because the illustrated embodimentof the ISP front-end 80 provides only a single pixel processing unit(FEProc), FEProc may be configured to alternate between processingframes corresponding to Sensor0 input data and frames corresponding toSensor1 input data. As will be appreciated, the image frames are readfrom FEProc in the illustrated embodiment to avoid a condition in whichimage data from one sensor is processed in real-time while image datafrom the other sensor is not processed in real-time. For instance, asshown in Table 3 below, which depicts one possible configuration ofNextVld fields in the go registers for each source when the ISP-frontend 80 is operating in a dual sensor mode, input data from each sensoris sent to memory (SIf0DMA and SIf1DMA) and to the correspondingstatistics processing unit (StatsPipe0 and StatsPipe1).

TABLE 3 NextVld per source example: Dual sensor mode SIf0 SIf1 StatsPipeStatsPipe DMA DMA 0 1 FEProc FEOut Sens0Go 1 X 1 0 0 0 Sens1Go X 1 0 1 00 Sens0DMAGo X X 0 X X X Sens1DMAGo X X X 0 X X ProcInDMAGo X X X X 1 0

The sensor frames in memory are sent to FEProc from the ProcInDMAsource, such that they alternate between Sensor0 and Sensor1 at a ratebased on their corresponding frame rates. For instance, if Sensor0 andSensor1 are both acquiring image data at a rate of 30 frames per second(fps), then their sensor frames may be interleaved in a 1-to-1 manner.If Sensor0 (30 fps) is acquiring image data at a rate twice that ofSensor1 (15 fps), then the interleaving may be 2-to-1, for example. Thatis, two frames of Sensor0 data are read out of memory for every oneframe of Sensor1 data.

With this in mind, FIG. 18 depicts a method 252 for processing framedata in the ISP front-end 80 having two sensors acquiring image datasimultaneously. At step 254, both Sensor0 and Sensor1 begin acquiringimage frames. As will be appreciated, Sensor0 and Sensor1 may acquirethe image frames using different frame rates, resolutions, and so forth.At step 256, the acquired frames from Sensor0 and Sensor1 written tomemory 108 (e.g., using SHODMA and SIf1DMA destinations). Next, sourceProcInDMA reads the frame data from the memory 108 in an alternatingmanner, as indicated at step 258. As discussed, frames may alternatebetween Sensor0 data and Sensor1 data depending on frame rate at whichthe data is acquired. At step 260, the next frame from ProcInDMA isacquired. Thereafter, at step 262, the NextVld and NextBk fields of thego register corresponding to the source, here ProcInDMA, is programmeddepending on whether the next frame is Sensor0 or Sensor1 data.Thereafter, decision logic 264 determines whether a source triggeringevent has occurred. As discussed above, data input from memory may betriggered by arming the go bit (e.g., “trigger-on-go” mode). Thus,triggering may occur once the go bit of the go register is set to 1.Once triggering occurs, the next frame becomes the current frame and issent to FEProc for processing at step 266. As discussed above, FEProcmay be configured using data parameters based on a corresponding dataregister (ProcPipe) specified in the NextBk field of the ProcInDMAGoregister. After processing of the current frame is completed at step268, the method 252 may return to step 260 and continue.

A further operational event that the ISP front-end 80 is configured tohandle is a configuration change during image processing. For instance,such an event may occur when the ISP front-end 80 transitions from asingle sensor configuration to a dual sensor configuration, orvice-versa. As discussed above, the NextVld fields for certain sourcesmay be different depending on whether one or both image sensors areactive. Thus, when the sensor configuration is changed, the ISPfront-end control unit 190 may release all destination units before theyare targeted by a new source. This may avoid invalid configurations(e.g., assigning multiple sources to one destination). In oneembodiment, the release of the destination units may be accomplished bysetting the NextVld fields of all the go registers to 0, thus disablingall destinations, and arming the go bit. After the destination units arereleased, the go registers may be reconfigured depending on the currentsensor mode, and image processing may continue.

A method 270 for switching between single and dual sensor configurationsis shown in FIG. 19, in accordance with one embodiment. Beginning atstep 272, a next frame of image data from a particular source of the ISPfront-end 80 is identified. At step 274, the target destinations(NextVld) are programmed into the go register corresponding to thesource. Next, at step 276, depending on the target destinations, NextBkis programmed to point to the correct data registers associated with thetarget destinations. Thereafter, decision logic 278 determines whether asource triggering event has occurred. Once triggering occurs, the nextframe is sent to the destination units specified by NextVld andprocessed by the destination units using the corresponding dataregisters specified by NextBk, as shown at step 280. The processingcontinues until step 282, at which the processing of the current frameis completed.

Subsequently, decision logic 284 determines whether there is a change inthe target destinations for the source. As discussed above, NextVldsettings of the go registers corresponding to Sens0 and Sens1 may varydepending on whether one sensor or two sensors are active. For instance,referring to Table 2, if only Sensor0 is active, Sensor0 data is sent toSIf0DMA, StatsPipe0, and FEProc. However, referring to Table 3, if bothSensor0 and Sensor1 are active, then Sensor0 data is not sent directlyto FEProc. Instead, as mentioned above, Sensor0 and Sensor 1 data iswritten to memory 108 and is read out to FEProc in an alternating mannerby source ProcInDMA. Thus, if no target destination change is detectedat decision logic 284, the control unit 190 deduces that the sensorconfiguration has not changed, and the method 270 returns to step 276,whereat the NextBk field of the source go register is programmed topoint to the correct data registers for the next frame, and continues.

If, however, at decision logic 284, a destination change is detected,then the control unit 190 determines that a sensor configuration changehas occurred. For instance, this could represent switching from singlesensor mode to dual sensor mode, or shutting off the sensors altogether.Accordingly, the method 270 continues to step 286, at which all bits ofthe NextVld fields for all go registers are set to 0, thus effectivelydisabling the sending of frames to any destination on the next trigger.Then, at decision logic 288, a determination is made as to whether alldestination units have transition to an idle state. If not, the method270 waits at decision logic 288 until all destinations units havecompleted their current operations. Next, at decision logic 290, adetermination is made as to whether image processing is to continue. Forinstance, if the destination change represented the deactivation of bothSensor0 and Sensor1, then image processing ends at step 292. However, ifit is determined that image processing is to continue, then the method270 returns to step 274 and the NextVld fields of the go registers areprogrammed in accordance with the current operation mode (e.g., singlesensor or dual sensor). As shown here, the steps 284-292 for clearingthe go registers and destination fields may collectively be referred toby reference number 294.

Next, FIG. 20 shows a further embodiment by way of the flow chart(method 296) that provides for another dual sensor mode of operation.The method 296 depicts a condition in which one sensor (e.g., Sensor0)is actively acquiring image data and sending the image frames to FEProc150 for processing, while also sending the image frames to StatsPipe0and/or memory 108 (Sif0DMA), while the other sensor (e.g., Sensor1) isinactive (e.g., turned off), as shown at step 298. Decision logic 300then detects for a condition in which Sensor1 will become active on thenext frame to send image data to FEProc. If this condition is not met,then the method 296 returns to step 298. However, if this condition ismet, then the method 296 proceeds by performing action 294 (collectivelysteps 284-292 of FIG. 19), whereby the destination fields of the sourcesare cleared and reconfigured at step 294. For instance, at step 294, theNextVld field of the go register associated with Sensor 1 may beprogrammed to specify FEProc as a destination, as well as StatsPipe1and/or memory (Sif1DMA), while the NextVld field of the go registerassociated with Sensor0 may be programmed to clear FEProc as adestination. In this embodiment, although frames captured by Sensor0 arenot sent to FEProc on the next frame, Sensor0 may remain active andcontinue to send its image frames to StatsPipe0, as shown at step 302,while Sensor1 captures and sends data to FEProc for processing at step304. Thus, both sensors, Sensor0 and Sensor1 may continue to operate inthis “dual sensor” mode, although only image frames from one sensor aresent to FEProc for processing. For the purposes of this example, asensor sending frames to FEProc for processing may be referred to as an“active sensor,” a sensor that is not sending frame FEProc but is stillsending data to the statistics processing units may be referred to as a“semi-active sensor,” and a sensor that is not acquiring data at all maybe referred to as an “inactive sensor.”

One benefit of the foregoing technique is that the because statisticscontinue to be acquired for the semi-active sensor (Sensor0), the nexttime the semi-active sensor transitions to an active state and thecurrent active sensor (Sensor 1) transitions to a semi-active orinactive state, the semi-active sensor may begin acquiring data withinone frame, since color balance and exposure parameters may already beavailable due to the continued collection of image statistics. Thistechnique may be referred to as “hot switching” of the image sensors,and avoids drawbacks associated with “cold starts” of the image sensors(e.g., starting with no statistics information available). Further, tosave power, since each source is asynchronous (as mentioned above), thesemi-active sensor may operate at a reduced clock and/or frame rateduring the semi-active period.

Before continuing with a more detailed description of the statisticsprocessing and pixel processing operations depicted in the ISP front-endlogic 80 of FIG. 10, it is believed that a brief introduction regardingseveral types of memory addressing formats that may be used inconjunction with the presently disclosed techniques, as well as adefinition of various ISP frame regions, will help to facilitate abetter understanding of the present subject matter.

Referring now to FIGS. 21 and 22, a linear addressing mode and a tiledaddressing mode that may be applied to pixel data received from theimage sensor(s) 90 and stored into memory (e.g., 108) are illustrated,respectively. In the depicted embodiment may be based upon a hostinterface block request size of 64 bytes. As will be appreciated, otherembodiments may utilize different block request sizes (e.g., 32 bytes,128 bytes, and so forth). In the linear addressing mode shown in FIG.21, image samples are located in memory in sequential order. The term“linear stride” specifies the distance in bytes between 2 adjacentvertical pixels. In the present example, the starting base address of aplane is aligned to a 64-byte boundary and the linear stride may be amultiple of 64 (based upon the block request size).

In the example of a tiled mode format, as shown in FIG. 22, the imagesamples are first arranged sequentially in “tiles,” which are thenstored in memory sequentially. In the illustrated embodiment, each tilemay be 256 bytes wide by 16 rows high. The term “tile stride” should beunderstood to refer to the distance in bytes between 2 adjacent verticaltiles. In the present example, the starting base address of a plane intiled mode is aligned to a 4096 byte boundary (e.g., the size of a tile)and the tile stride may be a multiple of 4096.

With this in mind, various frame regions that may be defined within animage source frame are illustrated in FIG. 23. The format for a sourceframe provided to the image processing circuitry 32 may use either thetiled or linear addressing modes discussed above, as may utilize pixelformats in 8, 10, 12, 14, or 16-bit precision. The image source frame306, as shown in FIG. 23, may include a sensor frame region 308, a rawframe region 308, and an active region 310. The sensor frame 308 isgenerally the maximum frame size that the image sensor 90 can provide tothe image processing circuitry 32. The raw frame region 310 may bedefined as the region of the sensor frame 308 that is sent to the ISPfront-end processing logic 80. The active region 312 may be defined as aportion of the source frame 306, typically within the raw frame region310, on which processing is performed for a particular image processingoperation. In accordance with embodiments of the present technique, thatactive region 312 may be the same or may be different for differentimage processing operations.

In accordance with aspects of the present technique, the ISP front-endlogic 80 only receives the raw frame 310. Thus, for the purposes of thepresent discussion, the global frame size for the ISP front-endprocessing logic 80 may be assumed as the raw frame size, as determinedby the width 314 and height 316. In some embodiments, the offset fromthe boundaries of the sensor frame 308 to the raw frame 310 may bedetermined and/or maintained by the control logic 84. For instance, thecontrol logic 84 may be include firmware that may determine the rawframe region 310 based upon input parameters, such as the x-offset 318and the y-offset 320, that are specified relative to the sensor frame308. Further, in some cases, a processing unit within the ISP front-endlogic 80 or the ISP pipe logic 82 may have a defined active region, suchthat pixels in the raw frame but outside the active region 312 will notbe processed, i.e., left unchanged. For instance, an active region 312for a particular processing unit having a width 322 and height 324 maybe defined based upon an x-offset 326 and y-offset 328 relative to theraw frame 310. Further, where an active region is not specificallydefined, one embodiment of the image processing circuitry 32 may assumethat the active region 312 is the same as the raw frame 310 (e.g.,x-offset 326 and y-offset 328 are both equal to 0). Thus, for thepurposes of image processing operations performed on the image data,boundary conditions may be defined with respect to the boundaries of theraw frame 310 or active region 312. Additionally, in some embodiments, awindow (frame) may be specified by identifying a starting and endinglocation in memory, rather than a starting location and window sizeinformation.

In some embodiments, the ISP front-end processing unit (FEProc) 80 mayalso support the processing an image frame by way of overlappingvertical stripes, as shown in FIG. 24. For instance, image processing inthe present example may occur in three passes, with a left stripe(Stripe0), a middle stripe (Stripe1), and a right stripe (Stripe2). Thismay allow the ISP front-end processing unit 80 to process a wider imagein multiple passes without the need for increasing line buffer size.This technique may be referred to as “stride addressing.”

When processing an image frame by multiple vertical stripes, the inputframe is read with some overlap to allow for enough filter contextoverlap so that there is little or no difference between reading theimage in multiple passes versus a single pass. For instance, in thepresent example, Stripe0 with a width SrcWidth0 and Stripe1 with a widthSrcWidth1 partially overlap, as indicated by the overlapping region 330.Similarly, Stripe1 also overlaps on the right side with Stripe2 having awidth of SrcWidth2, as indicated by the overlapping region 332. Here,the total stride is the sum of the width of each stripe (SrcWidth0,SrcWidth1, SrcWidth2) minus the widths (334, 336) of the overlappingregions 330 and 332. When writing the image frame to memory (e.g., 108),an active output region is defined and only data inside the outputactive region is written. As shown in FIG. 24, on a write to memory,each stripe is written based on non-overlapping widths of ActiveDst0,ActiveDst1, and ActiveDst2.

As discussed above, the image processing circuitry 32 may receive imagedata directly from a sensor interface (e.g., 94) or may receive imagedata from memory 108 (e.g., DMA memory). Where incoming data is providedfrom memory, the image processing circuitry 32 and the ISP front-endprocessing logic 80 may be configured to provide for byte swapping,wherein incoming pixel data from memory may be byte swapped beforeprocessing. In one embodiment, a swap code may be used to indicatewhether adjacent double words, words, half words, or bytes of incomingdata from memory are swapped. For instance, referring to FIG. 25, byteswapping may be performed on a 16 byte (bytes 0-15) set of data using afour-bit swap code.

As shown, the swap code may include four bits, which may be referred toas bit3, bit2, bit1, and bit0, from left to right. When all bits are setto 0, as shown by reference number 338, no byte swapping is performed.When bit3 is set to 1, as shown by reference number 340, double words(e.g., 8 bytes) are swapped. For instance, as shown in FIG. 25, thedouble word represented by bytes 0-7 is swapped with the double wordrepresented by bytes 8-15. If bit2 is set to 1, as shown by referencenumber 342, word (e.g., 4 bytes) swapping is performed. In theillustrated example, this may result in the word represented by bytes8-11 being swapped with the word represented by bytes 12-15, and theword represented by bytes 0-3 being swapped with the word represented bybytes 4-7. Similarly, if bit1 is set to 1, as shown by reference number344, then half word (e.g., 2 bytes) swapping is performed (e.g., bytes0-1 swapped with bytes 2-3, etc.) and if bit° is set to 1, as shown byreference number 346, then byte swapping is performed.

In the present embodiment, swapping may be performed in by evaluatingbits 3, 2, 1, and 0 of the swap code in an ordered manner. For example,if bits 3 and 2 are set to a value of 1, then double word swapping(bit3) is first performed, followed by word swapping (bit2). Thus, asshown in FIG. 25, when the swap code is set to “1111,” the end result isthe incoming data being swapped from little endian format to big endianformat.

Next, various memory formats for image pixel data that may be supportedby the image processing circuitry 32 for raw image data (e.g., Bayer RGBdata), RGB color data, and YUV (YCC, luma/chroma data) are discussed infurther detail in accordance with certain disclosed embodiments. First,formats for raw image pixels (e.g., Bayer data prior to demosaicing) ina destination/source frame that may be supported by embodiments of theimage processing circuitry 32 are discussed. As mentioned, certainembodiments may support processing of image pixels at 8, 10, 12, 14, and16-bit precision. In the context of raw image data, 8, 10, 12, 14, and16-bit raw pixel formats may be referred to herein as RAW8, RAW10,RAW12, RAW14, and RAW16 formats, respectively. Examples showing how eachof the RAW8, RAW10, RAW12, RAW14, and RAW16 formats may be stored inmemory are shown graphically unpacked forms in FIG. 26. For raw imageformats having a bit-precision greater than 8 bits (and not being amultiple of 8-bits), the pixel data may also be stored in packedformats. For instance, FIG. 27 shows an example of how RAW10 imagepixels may be stored in memory. Similarly, FIG. 28 and FIG. 29illustrate examples by which RAW12 and RAW14 image pixels may be storedin memory. As will be discussed further below, when image data is beingwritten to/read from memory, a control register associated with thesensor interface 94 may define the destination/source pixel format,whether the pixel is in a packed or unpacked format, addressing format(e.g., linear or tiled), and the swap code. Thus, the manner in whichthe pixel data is read and interpreted by, the ISP processing circuitry32 may depend on the pixel format.

The image signal processing (ISP) circuitry 32 may also support certainformats of RGB color pixels in the sensor interface source/destinationframe (e.g., 310). For instance, RGB image frames may be received fromthe sensor interface (e.g., in embodiments where the sensor interfaceincludes on-board demosaicing logic) and saved to memory 108. In oneembodiment, the ISP front-end processing logic 80 (FEProc) may bypasspixel and statistics processing when RGB frames are being received. Byway of example only, the image processing circuitry 32 may support thefollowing RGB pixel formats: RGB-565 and RGB-888. An example of howRGB-565 pixel data may be stored in memory is shown in FIG. 30. Asillustrated, the RGB-565 format may provide one plane of an interleaved5-bit red color component, 6-bit green color component, and 5-bit bluecolor component in RGB order. Thus, 16 bits total may be used torepresent an RGB-565 pixel (e.g., {R0, G0, B0} or {R1, G1, B1}).

An RGB-888 format, as depicted in FIG. 31, may include one plane ofinterleaved 8-bit red, green, and blue color components in RGB order. Inone embodiment, the ISP circuitry 32 may also support an RGB-666 format,which generally provides one plane of interleaved 6-bit red, green andblue color components in RGB order. In such an embodiment, when anRGB-666 format is selected, the RGB-666 pixel data may be stored inmemory using the RGB-888 format shown in FIG. 31, but with each pixelleft justified and the two least significant bits (LSB) set as zero.

In certain embodiments, the ISP circuitry 32 may also support RGB pixelformats that allow pixels to have extended range and precision offloating point values. For instance, in one embodiment, the ISPcircuitry 32 may support the RGB pixel format shown in FIG. 32, whereina red (R0), green (G0), and blue (B0) color component is expressed as an8-bit value, with a shared 8-bit exponent (E0). Thus, in such anembodiment, the actual red (R′), green (G′) and blue (B′) values definedby R0, G0, B0, and E0 may be expressed as:

R′=R0[7:0]*2̂E0[7:0]

G′=G0[7:0]*2̂E0[7:0]

B′=B0[7:0]*2̂E0[7:0]

This pixel format may be referred to as the RGBE format, which is alsosometimes known as the Radiance image pixel format.

FIGS. 33 and 34 illustrate additional RGB pixel formats that may besupported by the ISP circuitry 32. Particularly, FIG. 33 depicts a pixelformat that may store 9-bit red, green, and blue components with a 5-bitshared exponent. For instance, the upper eight bits [8:1] of each red,green, and blue pixel are stored in respective bytes in memory. Anadditional byte is used to store the 5-bit exponent (e.g., E0[4:0]) andthe least significant bit [0] of each red, green, and blue pixel. Thus,in such an embodiment, the actual red (R′), green (G′) and blue (B′)values defined by R0, G0, B0, and E0 may be expressed as:

R′=R0[8:0]*2̂E0[4:0]

G′=G0[8:0]*2̂E0[4:0]

B′=B0[8:0]*2̂E0[4:0]

Further, the pixel format illustrated in FIG. 33 is also flexible inthat it may be compatible with the RGB-888 format shown in FIG. 31. Forexample, in some embodiments, the ISP processing circuitry 32 mayprocess the full RGB values with the exponential component, or may alsoprocess only the upper 8-bit portion [7:1] of each RGB color componentin a manner similar to the RGB-888 format.

FIG. 34 depicts a pixel format that may store 10-bit red, green, andblue components with a 2-bit shared exponent. For instance, the upper8-bits [9:2] of each red, green, and blue pixel are stored in respectivebytes in memory. An additional byte is used to store the 2-bit exponent(e.g., E0[1:0]) and the least significant 2-bits [1:0] of each red,green, and blue pixel. Thus, in such an embodiment, the actual red (R′),green (G′) and blue (B′) values defined by R0, G0, B0, and E0 may beexpressed as:

R′=R0[9:0]*2̂E0[1:0]

G′=G0[9:0]*2̂E0[1:0]

B′=B0[9:0]*2̂E0[1:0]

Additionally, like the pixel format shown in FIG. 33, the pixel formatillustrated in FIG. 34 is also flexible in that it may be compatiblewith the RGB-888 format shown in FIG. 31. For example, in someembodiments, the ISP processing circuitry 32 may process the full RGBvalues with the exponential component, or may also process only theupper 8-bit portion (e.g., [9:2]) of each RGB color component in amanner similar to the RGB-888 format.

The ISP circuitry 32 may also further support certain formats of YCbCr(YUV) luma and chroma pixels in the sensor interface source/destinationframe (e.g., 310). For instance, YCbCr image frames may be received fromthe sensor interface (e.g., in embodiments where the sensor interfaceincludes on-board demosaicing logic and logic configured to convert RGBimage data into a YCC color space) and saved to memory 108. In oneembodiment, the ISP front-end processing logic 80 may bypass pixel andstatistics processing when YCbCr frames are being received. By way ofexample only, the image processing circuitry 32 may support thefollowing YCbCr pixel formats: YCbCr-4:2:0 8, 2, plane; and YCbCr-4:2:28, 1 plane.

The YCbCr-4:2:0, 2 plane pixel format may provide two separate imageplanes in memory, one for luma pixels (Y) and one for chroma pixels (Cb,Cr), wherein the chroma plane interleaves the Cb and Cr pixel samples.Additionally, the chroma plane may be sub-sampled by one-half in boththe horizontal (x) and vertical (y) directions. An example showing howYCbCr-4:2:0, 2 plane, data may be stored in memory is shown in FIG. 35,which depicts a luma plane 347 for storing the luma (Y) samples and achroma plane 348 for storing chroma (Cb, Cr) samples. A YCbCr-4:2:2 8, 1plane, format, which is shown in FIG. 36, may include one image plane ofinterleaved luma (Y) and chroma (Cb, Cr) pixel samples, with the chromasamples being sub-sampled by one-half both the horizontal (x) andvertical (y) directions. In some embodiment, the ISP circuitry 32 mayalso support 10-bit YCbCr pixel formats by saving the pixel samples tomemory using the above-described 8-bit format with rounding (e.g., thetwo least significant bits of the 10-bit data are rounded off). Further,as will be appreciated, YC1C2 values may also be stored using any of theRGB pixel formats discussed above in FIGS. 30-34, wherein each of the Y,C1, and C2 components are stored in a manner analogous to an R, G, and Bcomponent.

Referring back to the ISP front-end processing logic 80 shown in FIG.10, various read and write channels to memory 108 are provided. In oneembodiment, the read/write channels may share a common data bus, whichmay be provided using Advanced Microcontroller Bus Architecture, such asan Advanced Extensible Interface (AXI) bus, or any other suitable typeof bus (AHB, ASB, APB, ATB, etc.). Depending on the image frameinformation (e.g., pixel format, address format, packing method) which,as discussed above, may be determined via a control register, an addressgeneration block, which may be implemented as part of the control logic84, may be configured to provide address and burst size information tothe bus interface. By way of example the address calculation may dependvarious parameters, such as whether the pixel data is packed orunpacked, the pixel data format (e.g., RAW8, RAW10, RAW12, RAW14, RAW16,RGB, or YCbCr/YUV formats), whether tiled or linear addressing format isused, x- and y-offsets of the image frame data relative to the memoryarray, as well as frame width, height, and stride. Further parametersthat may be used in calculation pixel addresses may include minimumpixel unit values (MPU), offset masks, a byte per MPU value (BPPU), anda Log 2 of MPU value (L2MPU). Table 4, which is shown below, illustratesthe aforementioned parameters for packed and unpacked pixel formats, inaccordance with one embodiment.

TABLE 4 Pixel Address Calculation Parameters (MPU, L2MPU, BPPU) MPUL2MPU BPPU (Minimum (Log2 (Bytes Format Pixel Unit) of MPU) OffsetMaskPer MPU) RAW8 Unpacked 1 0 0 1 RAW16 Unpacked 1 0 0 2 RAW10 Packed 4 2 35 Unpacked 1 0 0 2 RAW12 Packed 4 2 3 6 Unpacked 1 0 0 2 RAW14 Packed 42 3 7 Unpacked 1 0 0 2 RGB-888 1 0 0 4 RGB-666 1 0 0 4 RGB-565 1 0 0 2YUV-4:2:0 (8-bit) 2 1 0 2 YUV-4:2:0 (10-bit) 2 1 0 2 YUV-4:2:2 (8-bit) 21 0 4 YUV-4:2:2 (10-bit) 2 1 0 4

As will be understood, the MPU and BPPU settings allow the ISP circuitry32 to assess the number of pixels that need to be read in order to readone pixel, even if not all of the read data is needed. That is, the MPUand BPPU settings may allow the ISP circuitry 32 read in pixel dataformats that are both aligned with (e.g., a multiple of 8 bits (1 byte)is used to store a pixel value) and unaligned with memory byte (e.g.,pixel values are stored using fewer or greater than a multiple of 8 bits(1 byte), i.e., RAW10, RAW12, etc.).

Referring to FIG. 37, an example showing the location of an image frame350 stored in memory under linear addressing is illustrated, which eachblock representing 64 bytes (as discussed above in FIG. 21). In oneembodiment, the following pseudo-code illustrates a process that may beimplemented by the control logic to identify a starting block and blockwidth of the stored frame in linear addressing:

BlockWidth=LastBlockX−BlockOffsetX+1;wherein

BlockOffsetX=(((OffsetX>>L2MPU)*BPPU)>>6)

LastBlockX=((((OffsetX+Width−1)>>L2MPU)*BPPU+BPPU−1)>>6)

BlockStart=OffsetY*Stride+BlockOffsetX

wherein Stride represents the frame strides in bytes and is a multipleof 64. For example, in FIG. 37, the SrcStride and DstStride is 4,meaning 4 blocks of 64 bytes. Referring to Table 4 above, the values forL2MPU and BPPU may depend on the format of the pixels in the frame 350.As shown, once BlockOffsetX is known, BlockStart may be determined.BlockWidth may subsequently be determined using BlockOffsetX andLastBlockX, which may be determined using the values of L2MPU and BPPUcorresponding to the pixel format of the frame 350.

A similar example under tiled addressing is depicted in FIG. 38, whereinthe source image frame, referred to here by reference number 352, isstored in memory and overlaps a portion of Tile0, Tile 1, Tile n, andTile n+1. In one embodiment, the following pseudo-code illustrates aprocess that may be implemented by the control logic to identify astarting block and block width of the stored frame in tiled addressing

     Block Width = LastBlockX − BlockOffsetX + 1; wherein     BlockOffsetX = (((OffsetXL 2 MPU) * BPPU)6)LastBlockX = ((((OffsetX + Width − 1)L 2 MPU) * BPPU + BPPU − 1)6)BlockStart = ((OffsetY4) * (Stride6) + (BlockOffsetX2) * 64 + OffsetY[3:0] * 4 + (BlockOffsetX[1:0])

In the above-depicted calculation, the expression“(OffsetY>>4)*(Stride>>6)” may represent the number of blocks to get totile row in which the image frame is located in memory. The expression“(BlockOffsetX>>2)*64” may represent the number of blocks that thestored image frame is offset in the x-direction. The expression“OffsetY[3:0]*4” may represent the number of blocks to get to a rowwithin a tile in which the starting address of the image frame islocated. Further, the expression “BlockOffsetX[1:0]” may represent thenumber of blocks to get to an x-offset within a tile corresponding tothe starting address of the image frame. Additionally, in the embodimentillustrated in FIG. 38, the number of blocks for each tile(BlocksPerTile) may be 64 blocks, and the number of bytes per block(BytesPerBlock) may be 64 bytes.

As shown above in Table 4, for pixels stored in RAW10, RAW12 and RAW14packed formats, four pixels make a minimum pixel unit (MPU) of five,six, or seven bytes (BPPU), respectively. For instance, referring to theRAW10 pixel format example shown in FIG. 27, an MPU of four pixels P0-P3includes 5 bytes, wherein the upper 8 bits of each of the pixels P0-P3are stored in four respective bytes, and the lower 2 bytes of each ofthe pixels are stored in bits 0-7 of the 32-bit address 01h. Similarly,referring back to FIG. 28, an MPU of four pixels P0-P3 using the RAW12format includes 6 bytes, with the lower 4 bits of pixels P0 and P1 beingstored in the byte corresponding to bits 16-23 of address 00h and thelower 4 bits of pixels P2 and P3 being stored in the byte correspondingto bits 8-15 of address 01h. FIG. 29 shows an MPU of four pixels P0-P3using the RAW14 format as including 7 bytes, with 4 bytes for storingthe upper 8 bits of each pixel of the MPU and 3 bytes for storing thelower 6 bits of each pixel of the MPU.

Using these pixel formats, it is possible at the end of a frame line tohave a partial MPU where less than four pixels of the MPU are used(e.g., when the line width modulo four is non-zero). When reading apartial MPU, unused pixels may be ignored. Similarly, when writing apartial MPU to a destination frame, unused pixels may be written with avalue of zero. Further, in some instances, the last MPU of a frame linemay not align to a 64-byte block boundary. In one embodiment, bytesafter the last MPU and up to the end of the last 64-byte block are notwritten.

In accordance with embodiments of the present disclosure, the ISPprocessing circuitry 32 may also be configured to provide overflowhandling. For instance, an overflow condition (also referred to as“overrun”) may occur in certain situations where the ISP front-endprocessing logic 80 receives back-pressure from its own internalprocessing units, from downstream processing units (e.g., the ISPpipeline 82 and/or ISP back-end processing logic 120), or from adestination memory (e.g., where the image data is to be written).Overflow conditions may occur when pixel data is being read in (e.g.,either from the sensor interface or memory) faster than one or moreprocessing blocks is able to process the data, or faster than the datamay be written to a destination (e.g., memory 108).

As will be discussed further below, reading and writing to memory maycontribute to overflow conditions. However, since the input data isstored, in the case of an overflow condition, the ISP circuitry 32 maysimply stall the reading of the input data until the overflow conditionrecovers. However, when image data is being read directly from an imagesensor, the “live” data generally cannot be stalled, as the image sensoris generally acquiring the image data in real time. For instance, theimage sensor (e.g., 90) may operate in accordance with a timing signalbased upon its own internal clock and may be configured to output imageframes at a certain frame rate, such as 15 or 30 frames per second(fps). The sensor inputs to the ISP circuitry 32 and memory 108 may thusinclude input queues which may buffer the incoming image data before itis processed (by the ISP circuitry 32) or written to memory (e.g., 108).Accordingly, if image data is being received at the input queue fasterthan it can be read out of the queue and processed or stored (e.g.,written to memory), an overflow condition may occur. That is, if thebuffers/queues are full, additional incoming pixels cannot be bufferedand, depending on the overflow handling technique implemented, may bedropped.

FIG. 39 shows a block diagram of the ISP processing circuitry 32, andfocuses on features of the control logic 84 that may provide foroverflow handling in accordance with one embodiment. As illustrated,image data associated with Sensor0 90 a and Sensor1 90 b may be read infrom memory 108 (by way of interfaces 174 and 176, respectively) to theISP front-end processing logic 80 (FEProc), or may be provided to theISP front-end processing logic 80 directly from the respective sensorinterfaces. In the latter case, incoming pixel data from the imagesensors 90 a and 90 b may be passed to input queues 400 and 402,respectively, before being sent to the ISP front-end processing logic80.

When an overflow condition occurs, the processing block(s) (e.g., blocks80, 82, or 120) or memory (e.g., 108) in which the overflow occurred mayprovide a signal (as indicated by signals 405, 407, and 408) to set abit in an interrupt request (IRQ) register 404. In the presentembodiment, the IRQ register 404 may be implemented as part of thecontrol logic 84. Additionally, separate IRQ registers 404 may beimplemented for each of Sensor0 image data and Sensor1 image data. Basedon the value stored in the IRQ register 404, the control logic 84 may beable to determine which logic units within the ISP processing blocks 80,82, 120 or memory 108 generated the overflow condition. The logic unitsmay be referred to as “destination units,” as they may constitutedestinations to which pixel data is sent. Based on the overflowconditions, the control logic 84 may also (e.g., throughfirmware/software handling) govern which frames are dropped (e.g.,either not written to memory or not output to the display for viewing).

Once an overflow condition is detected, the manner in which overflowhandling is carried may depend on whether the ISP front-end is readingpixel data from memory 108 or from the image sensor input queues (e.g.,buffers) 400, 402, which may be first-in-first-out (FIFO) queues in oneembodiment. In one embodiment, when input pixel data is read from memory108 through, for example, an associated DMA interface (e.g., 174 or176), the ISP-front-end will stall the reading of the pixel data if itreceives back-pressure as a result of an overflow condition beingdetected (e.g., via control logic 84 using the IRQ register 404) fromany downstream destination blocks which may include the ISP pipeline 82,the ISP back-end processing logic 120, or the memory 108 in instanceswhere the output of the ISP front-end logic 80 is written to memory 108.In this scenario, the control logic 84 may prevent overflow by stoppingthe reading of the pixel data from memory 108 until the overflowcondition recovers. For instance, overflow recovery may be signaled whena downstream unit causing the overflow condition sets a correspondingbit in the IRQ register 404 indicating that overflow is no longeroccurring. An embodiment of this process is generally illustrated bysteps 412-420 of the method 410 of FIG. 40.

While overflow conditions may generally be monitored at the sensor inputqueues, it should be understood that many additional queues may bepresent between processing units of the ISP sub-system 32 (e.g.,including internal units of the ISP front-end logic 80, the ISP pipeline82, as well as the ISP back-end logic 120). Additionally, the variousinternal units of the ISP sub-system 32 may also include line buffers,which may also function as queues. Thus, all the queues and line buffersof the ISP sub-system 32 may provide buffering. Accordingly, when thelast processing block in a particular chain of processing blocks is full(e.g., its line buffers and any intermediate queues are full),back-pressure may be applied to the preceding (e.g., upstream)processing block and so forth, such that the back-pressure propagates upthrough the chain of logic until it reaches the sensor interface, whereoverflow conditions may be monitored. Thus, when an overflow occurs atthe sensor interface, it may mean that all the downstream queues andline buffers are full.

As shown in FIG. 40, the method 410 begins at block 412, at which pixeldata for a current from is read from memory to the ISP front-endprocessing unit 80. Decision logic 414 then determines whether anoverflow condition is present. As discussed above, this may be assessedby determining the state of bits in the IRQ register(s) 404. If nooverflow condition is detected, then the method 410 returns to step 412and continues to read in pixels from the current frame. If an overflowcondition is detected by decision logic 414, the method 410 stopsreading pixels of the current frame from memory, as shown at block 416.Next, at decision logic 418, it is determined whether the overflowcondition has recovered. If the overflow condition still persists, themethod 410 waits at decision logic 418 until the overflow conditionrecovers. If decision logic 418 indicates that the overflow conditionhas recovered, then the method 410 proceeds to block 420 and resumesreading pixel data for the current frame from memory.

When an overflow condition occurs while input pixel data is being readin from the sensor interface(s), interrupts may indicate whichdownstream units (e.g., processing blocks or destination memory)generated the overflow. In one embodiment, overflow handling may beprovided based on two scenarios. In a first scenario, the overflowcondition occurs during an image frame, but recovers prior to the startof the subsequent image frame. In this case, input pixels from the imagesensor are dropped until the overflow condition recovers and spacebecomes available in the input queue corresponding to the image sensor.The control logic 84 may provide a counter 406 which may keep track ofthe number of dropped pixels and/or dropped frames. When the overflowcondition recovers, the dropped pixels may be replaced with undefinedpixel values (e.g., all l's (e.g., 11111111111111 for an 14-bit pixelvalue), all 0's, or a value programmed into a data register that setswhat the undefined pixel values are), and downstream processing mayresume. In a further embodiment, the dropped pixels may be replaced witha previous non-overflow pixel (e.g., the last “good” pixel read into theinput buffer). This ensures that a correct number of pixels (e.g., anumber of pixels corresponding to the number of pixels expected in acomplete frame) is sent to the ISP front-end processing logic 80, thusenabling the ISP front-end processing logic 80 to output the correctnumber of pixels for the frame that was being read in from the sensorinput queue when the overflow occurred.

While the correct number of pixels may be output by the ISP front-endunder this first scenario, depending on the number of pixels that weredropped and replaced during the overflow condition, software handling(e.g., firmware), which may be implemented as part of the control logic84, may choose to drop (e.g., exclude) the frame from being sent to thedisplay and/or written to memory. Such a determination may be based, forexample, upon the value of the dropped pixel counter 406 compared to anacceptable dropped pixel threshold value. For instance, if an overflowcondition occurs only briefly during the frame such that only arelatively small amount of pixels are dropped (e.g., and replaced withundefined or dummy values; e.g., 10-20 pixels or less), then the controllogic 84 may choose to display and/or store this image despite the smallnumber of dropped pixels, even though the presence of the replacementpixels may appear very briefly as a minor artifact in the resultingimage. However, due to the small number of replacement pixels, such anartifact may go generally unnoticed or marginally perceivable by a user.That is, the presence any such artifacts due to the undefined pixelsfrom the brief overflow condition may not significantly degrade theaesthetic quality of the image (e.g., any such degradation is minimal ornegligible to the human eye).

In a second scenario, the overflow condition may remain present into thestart of the subsequent image frame. In this case, the pixels of thecurrent frame are also dropped and counted like the first scenariodescribed above. However, if an overflow condition is still present upondetecting a VSYNC rising edge (e.g., indicating the start of asubsequent frame), the ISP front-end processing logic 80 may beconfigured to hold off the next frame, thus dropping the entire nextframe. In this scenario, the next frame and subsequent frames willcontinue to be dropped until overflow recovers. Once the overflowrecovers, the previously current frame (e.g., the frame being read whenthe overflow was first detected) may replace its dropped pixels with theundefined pixel values, thus allowing the ISP front-end logic 80 tooutput the correct number of pixels for that frame. Thereafter,downstream processing may resume. As for the dropped frames, the controllogic 84 may further include a counter that counts the number of droppedframes. This data may be used to adjust timings for audio-videosynchronization. For instance, for video captured at 30 fps, each framehas a duration of approximately 33 milliseconds. Thus, if three framesare dropped due to overflow, then the control logic 84 may be configuredto adjust audio-video synchronization parameters to account for theapproximately 99 millisecond (33 milliseconds×3 frames) durationattributable to the dropped frames. For instance, to compensate for timeattributable due to the dropped frames, the control logic 84 may controlimage output by repeating one or more previous frames.

An embodiment of a process 430 showing the above-discussed scenariosthat may occur when input pixel data is being read from the sensorinterfaces is illustrated in FIG. 41. As shown, the method 430 begins atblock 432, at which pixel data for a current frame is read in from thesensor to the ISP front-end processing unit 80. Decision logic 434 thendetermines whether an overflow condition exists. If there is nooverflow, the method 430 continues to read in pixels of the currentframe (e.g., returns to block 432). If decision logic 434 determinesthat an overflow condition is present, then the method 430 continues toblock 436, where the next incoming pixel of the current frame isdropped. Next, decision logic 438 determines whether the current framehas ended and the next frame has begun. For instance, in one embodiment,this may include detecting a rising edge in the VSYNC signal. If thesensor is still sending the current frame, the method 430 continues todecision logic 440, which determines whether the overflow conditionoriginally detected at logic 434 is still present. If the overflowcondition has not recovered, then the method 430 proceeds to block 442,at which the dropped pixel counter is incremented (e.g., to account forthe incoming pixel dropped at block 436). The method then returns toblock 436 and continues.

If at decision logic 438, it is detected that the current frame hasended and that the sensor is sending the next frame (e.g., VSYNC risingdetected), then the method 430 proceeds to block 450, and the all pixelsof the next frame, and subsequent frames are dropped as long as theoverflow condition remains (e.g., shown by decision logic 452). Asdiscussed above, a separate counter may track the number of droppedframes, which may be used to adjust audio-video synchronizationparameters. If decision logic 452 indicates that the overflow conditionhas recovered, then the dropped pixels from the initial frame in whichthe overflow condition first occurred are replaced with a number ofundefined pixel values corresponding to the number of dropped pixelsfrom that initial frame, as indicated by the dropped pixel counter. Asmentioned above, the undefined pixel values may be all 1's all 0's, areplacement value programmed into a data register, or may take the valueof a previous pixel that was read prior to the overflow condition (e.g.,the last pixel read before the overflow condition was detected).Accordingly, this allows the initial frame to be processed with thecorrect number of pixels and, at block 446, downstream image processingmay continue, which may include writing the initial frame to memory. Asalso discussed above, depending on the number of pixels that weredropped in the frame, the control logic 84 may either choose to excludeor include the frame when outputting video data (e.g., if the number ofdropped pixels is above or below an acceptable dropped pixel threshold).As will be appreciated, overflow handling may be performed separatelyfor each input queue 400 and 402 of the ISP sub-system 32.

Another embodiment of overflow handling that may be implemented inaccordance with the present disclosure is shown in FIG. 42 by way of aflowchart depicting method 460. Here, overflow handling for an overflowcondition that occurs during a current frame but recovers prior to theend of a current frame is handled in the same manner as shown in FIG. 41and, therefore, those steps have thus been numbered with like referencenumbers 432-446. The difference between the method 460 of FIG. 42 andthe method 430 of FIG. 41 pertains to overflow handling when an overflowcondition continues into the next frame. For instance, referring todecision logic 438, when the overflow condition continues into the nextframe, rather than drop the next frame as in the method 430 of FIG. 41,the method 460 implements block 462, in which the dropped pixel counteris cleared, the sensor input queue is cleared, and the control logic 84is signaled to drop the partial current frame. By clearing the sensorinput queue and dropped pixel counter, the method 460 prepares toacquire the next frame (which now becomes the current frame), returningthe method to block 432. As will be appreciated, pixels for this currentframe may be read into the sensor input queue. If the overflow conditionrecovers before the input queue becomes full, then downstream processingresumes. However, if the overflow condition persists, the method 460will continue from block 436 (e.g., begin dropping pixels until overfloweither recovers or the next frame starts).

As mentioned above, the electronic device 10 may also provide for thecapture of audio data (e.g., via an audio capture device provided as oneof input structures 14) concurrently with image data (e.g., via imagingdevice 30 having image sensors 90). For instance, as showndiagrammatically in FIG. 43, audio data 470 and image data 472 mayrepresent video and audio data captured concurrently by the electronicdevice. The audio data 470 may include audio samples 474 captured overtime (t) and, similarly, the image data 472 may represent a series ofimage frames captured over time t. Each sample of the image data 472,referred to here by reference number 476, may represent a still imageframe. Thus, when the still image frames are viewed on chronologicalsuccession over time (e.g., a particular number of frames per second,such as 15-30 frames per second), a viewer will perceive the appearanceof a moving image, thus providing video data. When the audio data 470 isacquired and represented as digital data, it may be stored as binaryvalues representing samples (e.g., 474) of the amplitude of the audiosignal at equal time intervals. Further, though shown in FIG. 43 ashaving discrete divisions 474, it should be appreciated that audio data,in a practical implementation, may have a sample rate that issufficiently fast that the human ear perceives the audio data 470 ascontinuous sound.

During playback of the video data 472, the corresponding audio data 470may also be played back, thus allowing a viewer to not only view videodata of a captured event, but to also hear sound corresponding to thecaptured event. Ideally, the video data 472 and audio data 470 areplayed back in a synchronized manner. For instance, if the audio sampledesignated here as 474 a originally occurred at time t_(A) then, underideal playback conditions, an image frame originally captured at timet_(A) is output concurrently with the audio sample 474 a. However, ifsynchronization is not achieved, the viewer/listener may notice a timedelay or shift between the audio and video data. For instance, supposethat the audio sample 474 a is output with an image frame 476 coriginally captured at time t₀, which is chronologically earlier thantime t_(A). In this case, the audio data 470 is “ahead” of the videodata 472, and the user may experience a delay between hearing the audiosample from time t_(A) and seeing its expected corresponding videosample (image frame 476 a from time t_(A)), the delay being thedifference between times t_(A) and t_(o)). Similarly, suppose that theaudio sample 474 a is output with an image frame 476 b from time t_(B),which is chronologically later than time t_(A). In the latter case, theaudio data 470 is “behind” the video data 472, and the user mayexperience a delay between seeing the video sample (476 a) at time t_(A)and hearing its corresponding audio sample from time t_(A), the delaybeing the different between times t_(A) and t_(B)). These types ofdelays are sometimes referred to as “lip-sync” error. As will beappreciated, the latter two scenarios may negatively affect the userexperience. To achieve audio-video synchronization, a system isgenerally configured such that any compensation for synchronizationissues prioritizes audio over video, e.g., if a synchronization issue ispresent, image frames may be dropped or repeated without altering audio.

In some conventional systems, synchronization of audio and video data isperformed using start of frame interrupts (e.g., based on VSYNC signal).When such an interrupt occurs, indicating the start of a new frame, aprocessor may execute an interrupt service routine to service theinterrupt (e.g., clear bits), and a timestamp corresponding to when theinterrupt is serviced by the processor is associated with that frame. Aswill be appreciated, there is generally some latency between interruptrequest and the time in which the interrupt is serviced by theprocessor. Thus, a timestamp that is associated with a particular imageframe may reflect this latency, and thus may not actually represent theprecise time at which the frame actually started. Additionally, thislatency may be variable depending on processor load and bandwidth, whichmay further complicate audio-video synchronization issues.

As discussed above, the ISP front-end logic 80 may operate within itsown clock domain and provide an asynchronous interface to the sensorinterface 94 to support sensors of different sizes and having differenttiming requirements. To provide for synchronization of audio and videodata, the ISP processing circuitry 32 may utilize the ISP front-endclock to provide a counter that may be used to generate timestamps thatmay be associated with captured image frames. For instance, referring toFIG. 44, four registers, including a timer configuration register 490, atime code register 492, a Sensor0 time code register 494 and a Sensor1time code register 496, all of which may be utilized to providetimestamp functions in one embodiment based at least partially upon theclock for the ISP front-end processing logic 80. In one embodiment, theregister 490, 492, 494, and 496 may include 32-bit registers.

The time configuration register 490 may be configured to provide avalue, NClk, that may be used to provide a count for generating timestamp codes. In one embodiment, NClk may be a 4-bit value ranging frombetween 0-15. Based upon NClk, a timer or counter that indicates acurrent time code may be incremented by a value of one every 2̂NClk clockcycles (based on the ISP front-end clock domain). The current time codemay be stored in the time code register 492, thus providing for a timecode with 32-bits of resolution. The time code register 492 may also bereset by the control logic 84.

Referring briefly to FIG. 10, for each sensor interface input, Sif0 andSif1, the time code register 492 may be sampled when a rising edge isdetected on the vertical synchronization (VSYNC) signal (or if a fallingedge is detected depending on how VSYNC is configured), thus indicatingthe start of a new frame (e.g., at the end of a vertical blankinginterval). The time code corresponding to the VSYNC rising edge may bestored in either the time code register 494 or 496 depending on thesensor (Sensor0 or Sensor1) from which the image frame is provided, thusproviding a timestamp indicating the time at which capture of thecurrent frame capture began. In some embodiments, the VSYNC signal fromthe sensor may have a programmed or programmable delay. For instance, ifthe first pixel of the frame is delayed by n clock cycles, the controllogic 84 may be configured to compensate for this delay, such as byproviding an offset in hardware or using software/firmware compensation.Thus, the timestamp may be generated from the VSYNC rising edge with aprogrammed delay added. In another embodiment, the timestampcorresponding to the start of a frame could be determine using thefalling edge of the VSYNC signal with a programmable delay.

As the current frame is being processed, the control logic 84 read thetime stamp from the sensor time code register (494 or 496), and thetimestamp may be associated with the video image frame as a parameter inmetadata associated with the image frame. This is shown more clearly inFIG. 45, which provides a diagrammatical view of an image frame 476 andits associated metadata 498, which includes the timestamp 500 read fromthe appropriate time code register (e.g., register 494 for Sensor0 orregister 496 for Sensor1). In one embodiment, the control logic 84 maythen read the timestamp from the time code register when triggered by astart of frame interrupt. Thus, each image frame captured by the ISPprocessing circuitry 32 may have an associated timestamp based on theVSYNC signal. Control circuitry or firmware, which may be implemented aspart of the ISP control logic 84 or part of a separate control unit ofthe electronic device 10, may use the image frame timestamps to align orsynchronize a corresponding set of audio data, thus achievingaudio-video synchronization.

In some embodiments, the device 10 may include an audio processorconfigured to handle processing of audio data (e.g., audio data 470).For instance, the audio processor may be a standalone processing unit(e.g., part of processor(s) 16), or may be integrated with a mainprocessor, or may be part of a system-on-chip processing device. In suchembodiments, the audio processor and the image processing circuitry 32,which may be controlled by a processor (e.g., part of control logic 84)separate from the audio processor, may operate based on independentclocks. For instance, the clocks could be generated using separatephase-locked loops (PLL). Thus, for audio-video synchronizationpurposes, the device 10 may need to be able to correlate an imagetimestamp with an audio timestamp. In one embodiment, this correlationmay be accomplished using a main processor of the device 10 (e.g., aCPU). For example, the main processor may synchronize its own clock withthat of the audio processor and of the ISP circuitry 32 to determine thedifferent between the respective clocks of the audio processor and ISPcircuitry 32. This difference, once known, may be used to correlateaudio timestamps of the audio data (e.g., 470) with image frametimestamps of the image data (e.g., 472).

In one embodiment, the control logic 84 may also be configured to handlewrap-around conditions, such as when the maximum value of the 32-bittime code is reached, and wherein the next increment would require anadditional bit (e.g., 33-bits) to provide an accurate value. To providea simplified example, this type of wrap-around may occur when on afour-digit counter when the value 9999 is incremented and becomes 0000rather than 10000 due to the four-digit limitation. While the controllogic 84 may be capable of resetting the time code register 492, it maybe undesirable to do so when the wrap-around condition occurs while asession of video is still being captured. Thus, in such instances, thecontrol logic 84 may be include logic, which may be implemented bysoftware in one embodiment, configured to handle the wrap-aroundcondition by generating a higher precision timestamps (e.g., 64-bits)based upon the 32-bit register values. The software may generate thehigher precision timestamps, which may be written to the image framemetadata until the time code register 492 is reset. In one embodiment,the software may be configured to detect wrap-around and to add the timedifference resulting from the wrap-around condition to a higherresolution counter. For example, in one embodiment, when a wrap-aroundcondition is detected for a 32-bit counter, the software may sum themaximum value of the 32-bit counter (to account for the wrap around)with the current time value indicated by the 32-bit counter and storethe result in a higher resolution counter (e.g., greater than 32-bits).In such cases, the result in the high resolution counter may be writtento image metadata information until the 32-bit counter is reset.

FIG. 46 depicts a method 510 that generally describes the audio-videosynchronization techniques discussed above. As shown, the method 510begins at step 512, wherein pixel data is received from an image sensor(e.g., either Sensor0 or Sensor1). Next, at decision logic 514, adetermination is made as to whether the VSYNC signal indicates a startof a new frame. If a new frame is not detected, the method 510 returnsto step 512 and continues receiving pixel data from the current imageframe. If a new frame is detected at decision logic 514, then the method510 continues to step 516, at which the time code register (e.g.,register 492) is sampled to obtain a timestamp value corresponding tothe rising (or falling) edge of the VSYNC signal detected at step 514.Next, at step 518, the timestamp value is stored to the time coderegister (e.g., register 494 or 496) corresponding the image sensorproviding the input pixel data. Subsequently, at step 520, the timestampis associated with the metadata of the new image frame and, thereafter,the timestamp information in the image frame metadata may be used foraudio-video synchronization. For instance, the electronic device 10 maybe configured to provide audio-video synchronization by aligning videodata (using the timestamps of each individual frame) to thecorresponding audio data in a manner such that any delay betweencorresponding audio and video output is substantially minimized. Forinstance, as discussed above, a main processor of the device 10 may beutilized to determine how to correlate audio timestamps with videotimestamps. In one embodiment, if the audio data is ahead the videodata, image frames may be dropped to allow the correct image frame to“catch up” to the audio data stream and, if the audio data is behind thevideo data, image frames may be repeated to allow the audio data to“catch up” to the video stream.

Continuing to FIGS. 47 to 50, the ISP processing logic or sub-system 32may also be configured to provide for flash (also referred to as“strobe”) synchronization. For instance, when using a flash module,artificial lighting may be temporarily provided to aid in theillumination of an image scene. By way of example, the use of a flashmay be beneficial when capturing an image scene under low lightconditions. The flash or strobe may be provided using any suitablelighting source, such as an LED flash device or a xenon flash device,etc.

In the present embodiment, the ISP sub-system 32 may include a flashcontroller configured to control the timing and/or interval during whicha flash module is active. As will be appreciated, it is generallydesirable to control the timing and duration over which the flash moduleis active such that the flash interval starts before the first pixel ofa target frame (e.g., an image frame that is to be captured) is capturedand ends after the last pixel of the target frame is captured but beforethe start of a subsequent consecutive image frame. This helps to ensurethat all pixels within the target frame are exposed to similar lightingconditions while the image scene is being captured.

Referring to FIG. 47, a block diagram showing a flash controller 550implemented as part of the ISP sub-system 32 and configured to control aflash module 552 is illustrated in accordance with an embodiment of thepresent disclosure. In some embodiments, the flash module 552 mayinclude more than one strobe device. For instance, in certainembodiments, the flash controller 550 may be configured to provide apre-flash (e.g., for red-eye reduction), followed by a main flash. Thepre-flash and main flash events may be sequential, and may be providedusing the same or different strobe devices.

In the illustrated embodiment, timing of the flash module 552 may becontrolled based on timing information provided from the image sensors90 a and 90 b. For instance, the timing of an image sensor may becontrolled using a rolling shutter technique, whereby integration timeis governed using a slit aperture that scans over the pixel array of theimage sensor (e.g., 90 a and 90 b). Using the sensor timing information(shown here as reference number 556), which may be provided to the ISPsub-system 32 via the sensor interfaces 94 a and 94 b (each of which mayinclude a sensor-side interface 548 and a front-end-side interface 549),the control logic 84 may provide appropriate control parameters 554 tothe flash controller 550, which may then be utilized by the flashcontroller 550 for activating the flash module 552. As discussed above,by using sensor timing information 556, the flash controller 556 mayensure that the flash module is activated before the first pixel of thetarget frame is captured and remains activated for the duration of thetarget frame, with the flash module being deactivated after the lastpixel of the target frame is captured and prior to the start of the nextframe (e.g., VSYNC rising). This process may be referred to as “flashsynchronization” or “strobe synchronization,” techniques of which arediscussed further below.

Additionally, as shown in the embodiment of FIG. 47, the control logic84 may also utilize statistics data from the ISP front-end 80, shownhere as reference number 558, to determine whether present lightingconditions in the image scene corresponding to the target frame areappropriate for using the flash module. For instance, the ISP sub-system32 may utilize auto-exposure to try to maintain a target exposure level(e.g., light level) by adjusting integration time and/or sensor gains.However, as will be appreciated, integration time cannot be longer thanthe frame time. For instance, for video data acquired at 30 fps, eachframe has a duration of approximately 33 milliseconds. Thus, if a targetexposure level cannot be achieved using a maximum integration time,sensor gains may also be applied. However, if the adjustment of bothintegration time and sensor gains is unable to achieve a target exposure(e.g., if the light level is less than a target threshold), then theflash controller may be configured to activate the flash module.Further, in one embodiment, integration time may also be limited toavoid motion blur. For instance, while integration time may be extendedup to the duration of the frame, it could be further limited in someembodiments to avoid motion blur.

As discussed above, in order to ensure that the activation of the flashilluminates the target frame for its entire duration (e.g., that theflash is turned on prior to the first pixel of the target frame andturned off after the last pixel of the target frame), the ISP sub-system32 may utilize sensor timing information 556 to determine when toactivate/deactivate the flash 552

FIG. 48 shows depicts graphically how the sensor timing signal from theimage sensors 90 may be used to control flash synchronization. Forinstance, FIG. 48 shows a portion of an image sensor timing signal 556that may be provided by one of the image sensors 90 a or 90 b. Thelogical high portions of the signal 556 represent frame intervals. Forinstance, a first frame (FRAME N) is represented by reference number 570and a second frame (FRAME N+1) is represented by reference number 572.The actual time at which the first frame 570 starts is indicated by therising edge of the signal 556 at time t_(VSYNC) _(—) _(ra0) (e.g., with“r” designating a rising edge and “a” designating the “actual” aspect ofthe timing signal 556) and the actual time at which the first frame 570ends is indicated by the falling edge of the signal 556 at timet_(VSYNC) _(—) _(fa0) (e.g., with “f” designating a falling edge).Similarly, the actual time at which the second frame 572 starts isindicated by the rising edge of the signal 556 at time t_(VSYNC) _(—)_(ra1) and the actual time at which the second frame 572 ends isindicated by the falling edge of the signal 556 at time t_(VYNC) _(—)_(fa1). The interval 574 between the first and second frames may bereferred to as a blanking interval (e.g., vertical blanking), which mayallow image processing circuitry (e.g., ISP sub-system 32) to identifywhen image frames end and start. It should be appreciated that the frameintervals and vertical blanking intervals shown in the present figureare not necessarily drawn to scale.

As shown in FIG. 48, the signal 556 may represent the actual timing fromthe viewpoint of the image sensor 90. That is, the signal 556 representsthe timing at which frames are actually being acquired by the imagesensor. However, as the sensor timing information is provided todownstream components of the image processing system 32, delays may beintroduced into the sensor timing signal. For instance, the signal 576represents a delayed timing signal (delayed by a first time delay 578)that may be seen from the viewpoint of the sensor-side interface 548 ofthe interface logic 94 between the sensor 90 and the ISP front-endprocessing logic 80. The signal 580 may represent the delayed sensortiming signal from the viewpoint of the front-end-side interface 549,which is shown in FIG. 48 as being delayed relative to the sensor-sideinterface timing signal 572 by a second time delay 582, and delayedrelative to the original sensor timing signal 556 by a third time delay584, which is equal to the sum of the first time delay 578 and thesecond time delay 582. Next, as the signal 580 from the front-end-side549 of the interface 94 is provided to the ISP front-end processinglogic 80 (FEProc), an additional delay may be imparted such that fromthe delayed signal 588 is seen from the viewpoint of the ISP front-endprocessing logic 80. Specifically, the signal 588 seen by the ISPfront-end processing logic 80 is shown here as being delayed relative tothe delayed signal 580 (front-end-side timing signal) by a fourth timedelay 590, and delayed relative to the original sensor timing signal 556by a fifth time delay 592, which is equal to the sum of the first timedelay 578, the second time delay 582, and the fourth time delay 590.

For purposes of controlling flash timing, the flash controller 550 mayutilize the first signal available to the ISP front-end which is,therefore, shifted by the least amount of delay time relative to theactual sensor timing signal 556. Thus, in the present embodiment, theflash controller 550 may determine flash timing parameters based uponthe sensor timing signal 580, as seen from the viewpoint of thefront-end-side 549 of the sensor-to-ISP interface 94. Thus, the signal596, which is used by the flash controller 550 in the present example,may be identical to the signal 580. As shown, the delayed signal 596(delayed by the delay time 584 relative to signal 556) includes theframe intervals located between times t_(VSYNC) _(—) _(rd0) andt_(VSYNC) _(—) _(fd0) (e.g., where “d” represented “delayed”) whichcorrelate to the first frame 570 and between times t_(VSYNC) _(—) _(rd1)and t_(VSYNC) _(—) _(fd1), which correlate to the second frame 572. Asdiscussed above, it is generally desirable to activate the flash priorto the start of a frame and for the duration of the frame (e.g., todeactivate the flash after the last pixel of the frame) to ensure thatthe image scene is illuminated for the entirety of the frame, and toaccount for any warm-up time that the flash may need during activationto reach full intensity (which may be on the order of a microseconds(e.g., 100-800 microseconds) to a few milliseconds (e.g., 1-5millisecond)). However, since the signal 596 being analyzed by the flashcontroller 550 is delayed with respect to the actual timing signal 556,this delay is taken into account when determining flash timingparameters.

For instance, assuming that the flash is to be activated to illuminatethe image scene for the second frame 572, the delayed rising edge att_(YSYNC) _(—) _(rd1) occurs after the actual rising edge at t_(VSYNC)_(—) _(ra1). Thus, it may be difficult for the flash controller 550 touse the delayed rising edge t_(VSYNC) _(—) _(rd1) to determine a flashactivation starting time, as the delayed rising edge t_(YSYNC) _(—)_(rd1) occurs after the second frame 572 has already started (e.g.,after t_(VSYNC) _(—) _(ra1) of signal 556). In the present embodiment,the flash controller 550 may instead determine the flash activationstarting time based on the end of the previous frame, here the fallingedge at time t_(VSYNC) _(—) _(fd0). For instance, the flash controller550 may add a time interval 600 (which represents the vertical blankinginterval 574) to time t_(VSYNC) _(—) _(fd0), to calculate a time thatcorresponds to the delayed rising edge time t_(YSYNC) _(—) _(rd1) of theframe 572. As can be appreciated, the delayed rising edge time t_(VSYNC)_(—) _(rd1) occurs after the actual rising edge time t_(VSYNC) _(—)_(ra1) (signal 556) and, therefore, a time offset 598 (OffSet1), whichcorresponds to the time delay 584 of signal 580, is subtracted from thesum of time t_(YSYNC) _(—) _(fd0) and the blanking interval time 600.This produces a flash activation starting time that starts concurrentlywith the beginning of the second frame 572, at time t_(VSYNC) _(—)_(ra1). However, as mentioned above, depending on the type of flashdevice that is provided (e.g., xenon, LED, etc.), the flash module 552may experience a warm-up time between when the flash module is activatedand when the flash device reaches its full luminosity. The amount of thewarm-up time may depend on the type of flash device used (e.g., xenondevice, LED device, etc.). Thus, to account for such warm-up times, anadditional offset 602 (OffSet2), which may be programmed or preset(e.g., using a control register), may be subtracted from the beginningof the second frame 572, at time t_(VSYNC) _(—) _(ra1). This moves theflash activation starting time back to time 604, thus ensuring that theflash is activated (if needed to illuminate the scene) prior to thestart of the frame 572 being acquired by the image sensor. This processfor determining flash activation time may be expressed using the formulabelow:

t _(flash) _(—) _(start) _(—) _(frame1) =t _(VSYNC) _(—) _(fd0) +t_(vert) _(—) _(blank) _(—) _(int) −t _(OffSet)1−t _(OffSet)2

In the illustrated embodiment, the deactivation of the flash may occurat time t_(VSYNC) _(—) _(fd1) of the flash controller signal 596,provided that time t_(VSYNC) _(—) _(fd1) occurs prior to the start ofthe frame after frame 572 (e.g., FRAME N+2, which is not shown in FIG.48) as indicated by time 605 on the sensor timing signal 556. In otherembodiments, the deactivation of the flash may occur at a time (e.g., anoffset 606) after time t_(VSYNC) _(—) _(fd1) of signal 596 but beforethe start of the next frame (e.g., before a subsequent VSYNC rising edgeon the sensor timing signal 556 indicating the start of FRAME N+2), ormay occur within an interval 608 immediately prior to time t_(VSYNC)_(—) _(fd1), wherein the interval 608 is less than the amount of Offset1(598). As can be appreciated, this ensures that the flash remains on forthe entire duration of the target frame (e.g., frame 572).

FIG. 49 depicts a process 618 for determining a flash activation starttime on the electronic device 10 in accordance with the embodiment shownin FIG. 48. Beginning at block 620, a sensor timing signal (e.g., 556)from an image sensor is acquired and provided to flash control logic(e.g., flash controller 550), which may be part of an image signalprocessing sub-system (e.g., 32) of the electronic device 10. The sensortiming signal is provided to the flash control logic, but may be delayedwith respect the original timing signal (e.g., 556). At block 622, thedelay (e.g., delay 584) between the sensor timing signal and the delayedsensor timing signal (e.g., 596) is determined. Next, a target frame(e.g., frame 572) requesting flash illumination is identified at block624. To determine the time at which the flash module (e.g., 552) shouldbe activated to ensure that the flash is active prior to the start ofthe target frame, the process 618 then proceeds to block 626, at which afirst time (e.g., time t_(VSYNC) _(—) _(fd0)) corresponding to the endof the frame prior to the target frame, as indicated by the delayedtiming signal, is determined. Thereafter after, at block 628, the lengthof a blanking interval between frames is determined and added to thefirst time determined at block 626 to determine a second time. The delaydetermined at block 622 is then subtracted from the second time, asshown at block 630, to determine a third time. As discussed above, thissets the flash activation time to coincide with the actual start of thetarget frame in accordance with the non-delayed sensor timing signal.

In order to ensure that the flash is active prior to the start of thetarget frame, an offset (e.g., 602, Offset2) is subtracted from thethird time, as shown at block 632, to determine the desired flashactivation time. As will be appreciated, in some embodiments, the offsetfrom block 632 may not only ensure that the flash is on before thetarget frame, but may also compensate for any warm-up time that theflash may require between being initially activated and reaching fullluminosity. At block 634, the flash 552 is activated at the flash starttime determined at block 632. As discussed above and shown in block 636,the flash may remain on for the entire duration of the target frame, andmay be deactivated after the end of the target frame, so that all pixelsin the target frame are subject to similar lighting conditions. Whilethe embodiment described above in FIGS. 48 and 49 have discussed theapplication of flash synchronization techniques using a single flash, itshould be further appreciated that these flash synchronizationtechniques may also be applicable to embodiments of devices having twoor more flash devices (e.g., two LED flashes). For instance, if morethan one flash module is utilized, the above techniques may be appliedto both flash modules, such that each flash module is activated by theflash controller prior to the start of a frame and remain on for theduration of the frame (e.g., the flash modules may not necessarily beactivated for the same frames).

The flash timing techniques described herein may be applied whenacquiring images using the device 10. For instance, in one embodiment, apre-flash technique may be used during image acquisition. For example,when a camera or image acquisition application is active on the device10, the application may operate in a “preview” mode. In the previewmode, the image sensor(s) (e.g., 90) may be acquiring frames of imagedata which may be processed by the ISP sub-system 32 of the device 10for preview purposes (e.g., displaying on a display 28), although theframes may not actually be captured or stored until a capture request isinitiated by a user to place the device 10 into a “capture” mode. By wayof example, this may occur via user activation of a physical capturebutton on the device 10, or a soft-capture button, which may beimplemented via software as part of a graphical user interface anddisplayed on a display of the device 10 and being responsive to userinterface inputs (e.g., touch screen inputs).

Because the flash is not typically active during preview mode, thesudden activation of and the illumination of an image scene using aflash may, in some cases, significantly alter certain image statisticsfor a particular scene, such as those related to auto-white balancestatistics, etc., relative to the same image scene that is notilluminated by the flash. Thus, in order to improve statistics used toprocess a desired target frame, in one embodiment, a pre-flash operationtechnique may include receiving a user request to capture an image framethat requests flash illumination, using the flash at a first time toilluminate a first frame while the device 10 is still in preview mode,and updating the statistics (e.g., auto-white balance statistics) priorto the start of the next frame. The device 10 may enter capture mode andcapture the next frame using the updated statistics with the flashactivated, thus providing improved image/color accuracy.

FIG. 50 depicts a flow chart illustrating such a process 640 in moredetail. The process 640 begins at block 642 in which a request isreceived to capture an image using the flash. At block 644, the flash isactivated (e.g., may be timed using the techniques shown in FIGS. 48 and49) to illuminate a first frame while the device 10 is still in previewmode. Next, at block 646, image statistics, such as auto-white balancestatistics, are updated based statistics acquired from the illuminatedfirst frame. Thereafter, at block 648, the device 10 may enter thecapture mode and acquire the next frame using the updated imagestatistics from block 646. For instance, the updated image statisticsmay be used to determine white balance gains and/or color correctionmatrices (CCM), which may be used by firmware (e.g., control logic 84)to program the ISP pipeline 82. Thus, the frame (e.g., next frame)acquired at block 648 may be processed by the ISP pipeline 82 using oneor more parameters that are determined based upon the updated imagestatistics from block 646.

In another embodiment, color properties from a non-flash image scene(e.g., acquired or previewed without flash) may be applied whencapturing an image frame with flash. As will be appreciated, a non-flashimage scene generally exhibits better color properties relative to animage scene that is illuminated with the flash. The use of the flashmay, however, offer reduced noise and improved brightness (e.g., in lowlight conditions) relative to the non-flash image. However, the use ofthe flash may also result in some of the colors in the flash imageappearing somewhat washed out relative to a non-flash image of the samescene. Thus, in one embodiment, to retain the benefits of low noise andbrightness of a flash image while also partially retaining some of thecolor properties from the non-flash image, the device 10 may beconfigured to analyze a first frame without the flash to obtain itscolor properties. Then, the device 10 may capture a second frame usingthe flash and may apply a color palette transfer technique to the flashimage using the color properties from the non-flash image.

In certain embodiments, the device 10 configured to implement any of theflash/strobe techniques discussed above may be a model of an iPod®,iPhone®, iMac®, or MacBook® computing devices with integrated orexternal imaging devices, all of which are available from Apple Inc.Further, the imaging/camera application may be a version of the Camera®,iMovie®, or PhotoBooth® applications, also from Apple Inc.

Continuing to FIG. 51, a more detailed view of the ISP front-end pixelprocessing logic 150 (previously discussed in FIG. 10) is illustrated,in accordance with an embodiment of the present technique. As shown, theISP front-end pixel processing logic 150 includes a temporal filter 650and a binning compensation filter 652. The temporal filter 650 mayreceive one of the input image signals Sif0, Sif1, FEProcIn, orpre-processed image signals (e.g., 180, 184) and may operate on the rawpixel data before any additional processing is performed. For example,the temporal filter 650 may initially process the image data to reducenoise by averaging image frames in the temporal direction. The binningcompensation filter 652, which is discussed in more detail below, mayapply scaling and re-sampling on binned raw image data from an imagesensor (e.g., 90 a, 90 b) to maintain an even spatial distribution ofthe image pixels.

The temporal filter 650 may be pixel-adaptive based upon motion andbrightness characteristics. For instance, when pixel motion is high, thefiltering strength may be reduced in order to avoid the appearance of“trailing” or “ghosting artifacts” in the resulting processed image,whereas the filtering strength may be increased when little or no motionis detected. Additionally, the filtering strength may also be adjustedbased upon brightness data (e.g., “luma”). For instance, as imagebrightness increases, filtering artifacts may become more noticeable tothe human eye. Thus, the filtering strength may be further reduced whena pixel has a high level of brightness.

In applying temporal filtering, the temporal filter 650 may receivereference pixel data (Rin) and motion history input data (Hin), whichmay be from a previous filtered or original frame. Using theseparameters, the temporal filter 650 may provide motion history outputdata (Hout) and filtered pixel output (Yout). The filtered pixel outputYout is then passed to the binning compensation filter 652, which may beconfigured to perform one or more scaling operations on the filteredpixel output data Yout to produce the output signal FEProcOut. Theprocessed pixel data FEProcOut may then be forwarded to the ISP pipeprocessing logic 82, as discussed above.

Referring to FIG. 52, a process diagram depicting a temporal filteringprocess 654 that may be performed by the temporal filter shown in FIG.51 is illustrated, in accordance with a first embodiment. The temporalfilter 650 may include a 2-tap filter, wherein the filter coefficientsare adjusted adaptively on a per pixel basis based at least partiallyupon motion and brightness data. For instance, input pixels x(t), withthe variable “t” denoting a temporal value, may be compared to referencepixels r(t−1) in a previously filtered frame or a previous originalframe to generate a motion index lookup in a motion history table (M)655 that may contain filter coefficients. Additionally, based uponmotion history input data h(t−1), a motion history output h(t)corresponding to the current input pixel x(t) may be determined.

The motion history output h(t) and a filter coefficient, K, may bedetermined based upon a motion delta d(j,i,t), wherein (j,i) representcoordinates of the spatial location of a current pixel x(j,i,t). Themotion delta d(j,i,t) may be computed by determining the maximum ofthree absolute deltas between original and reference pixels for threehorizontally collocated pixels of the same color. For instance,referring briefly to FIG. 53, the spatial locations of three collocatedreference pixels 657, 658, and 659 that corresponding to original inputpixels 660, 661, and 662 are illustrated. In one embodiment, the motiondelta may be calculated based on these original and reference pixelsusing formula below:

d(j,i,t)=max3[abs(x(j,i−2,t)−r(j,i−2,t−1)),

(abs(x(j,i,t)−r(j,i,t−1)),

(abs(x(j,i+2,t)−r(j,i+2,t−1))]

A flow chart depicting this technique for determining the motion deltavalue is illustrated further below in FIG. 55. Further, it should beunderstood that the technique for calculating the motion delta value, asshown above in Equation 1a (and below in FIG. 55), is only intended toprovide one embodiment for determining a motion delta value.

In other embodiments, an array of same-colored pixels could be evaluatedto determine a motion delta value. For instance, in addition to thethree pixels referenced in Equation 1a, one embodiment for determiningmotion delta values may include also evaluating the absolute deltasbetween same colored pixels from two rows above (e.g., j−2; assuming aBayer pattern) the reference pixels 660, 661, and 662 and theircorresponding collocated pixels, and two rows below (e.g., j+2; assuminga Bayer pattern) the reference pixels 660, 661, and 662 and theircorresponding collocated pixels. For instance, in one embodiment, themotion delta value may be expressed as follows:

d(j,i,t)=max9[abs(x(j,i−2,t)−r(j,i−2,t−1)),

(abs(x(j,i,t)−r(j,i,t−1)),

(abs(x(j,i+2,t)−r(j,i+2,t−1)),

(abs(x(j−2,i−2,t)−r(j−2,i−2,t−1)),

(abs(x(j−2,i,t)−r(j−2,i,t−1)),

(abs(x(j−2,i+2,t)−r(j−2,i+2,t−1)),

(abs(x(j+2,i−2,t)−r(j+2,i−2,t−1))

(abs(x(j+2,i,t)−r(j+2,i,t−1)),

(abs(x(j+2,i+2,t)−r(j+2,i+2,t−1))]  (1b)

Thus, in the embodiment depicted by Equation 1b, the motion delta valuemay be determined by comparing the absolute delta between a 3×3 array ofsame-colored pixels, with the current pixel (661) being located at thecenter of the 3×3 array (e.g., really a 5×5 array for Bayer colorpatterns if pixels of different colors are counted). It should beappreciated, that any suitable two-dimensional array of same-coloredpixels (e.g., including arrays having all pixels in the same row (e.g.,Equation 1a) or arrays having all pixels in the same column) with thecurrent pixel (e.g., 661) being located at the center of the array couldbe analyzed to determine a motion delta value. Further, while the motiondelta value could be determined as the maximum of the absolute deltas(e.g., as shown in Equations 1a and 1b), in other embodiments, themotion delta value could also be selected as the mean or median of theabsolute deltas. Additionally, the foregoing techniques may also beapplied to other types of color filter arrays (e.g., RGBW, CYGM, etc.),and is not intended to be exclusive to Bayer patterns.

Referring back to FIG. 52, once the motion delta value is determined, amotion index lookup that may be used to selected the filter coefficientK from the motion table (M) 655 may be calculated by summing the motiondelta d(t) for the current pixel (e.g., at spatial location (j,i)) withthe motion history input h(t−1). For instance, the filter coefficient Kmay be determined as follows:

K=M[d(j,i,t)+h(j,i,t−1)]  (2a)

Additionally, the motion history output h(t) may be determined using thefollowing formula:

h(j,i,t)=d(j,i,t)+(1−K)×h(j,i,t−1)  (3a)

Next, the brightness of the current input pixel x(t) may be used togenerate a luma index lookup in a luma table (L) 656. In one embodiment,the luma table may contain attenuation factors that may be between 0 and1, and may be selected based upon the luma index. A second filtercoefficient, K′, may be calculated by multiplying the first filtercoefficient K by the luma attenuation factor, as shown in the followingequation:

K′=K×L[x(j,i,t)]  (4a)

The determined value for K′ may then be used as the filteringcoefficient for the temporal filter 650. As discussed above, thetemporal filter 650 may be a 2-tap filter. Additionally, the temporalfilter 650 may be configured as an infinite impulse response (IIR)filter using previous filtered frame or as a finite impulse response(FIR) filter using previous original frame. The temporal filter 650 maycompute the filtered output pixel y(t) (Yout) using the current inputpixel x(t), the reference pixel r(t−1), and the filter coefficient K′using the following formula:

y(j,i,t)=r(j,i,t−1)+K′(x(j,i,t)−r(j,i,t−1))  (5a)

As discussed above, the temporal filtering process 654 shown in FIG. 52may be performed on a pixel-by-pixel basis. In one embodiment, the samemotion table M and luma table L may be used for all color components(e.g., R, G, and B). Additionally, some embodiments may provide a bypassmechanism, in which temporal filtering may be bypassed, such as inresponse to a control signal from the control logic 84. Further, as willbe discussed below with respect to FIGS. 57 and 58, one embodiment ofthe temporal filter 650 may utilize separate motion and luma tables foreach color component of the image data.

The embodiment of the temporal filtering technique described withreference to FIGS. 52 and 53 may be better understood in view of FIG.54, which depicts a flow chart illustrating a method 664, in accordancewith the above-described embodiment. The method 664 begins at step 665,at which a current pixel x(t) located at spatial location (j,i) of acurrent frame of image data is received by the temporal filtering system654. At step 666, a motion delta value d(t) is determined for thecurrent pixel x(t) based at least partially upon one or more collocatedreference pixels (e.g., r(t−1)) from a previous frame of the image data(e.g., the image frame immediately preceding the current frame). Atechnique for determining a motion delta value d(t) at step 666 isfurther explained below with reference to FIG. 55, and may be performedin accordance with Equation 1a, as shown above.

Once the motion delta value d(t) from step 666 is obtained, a motiontable lookup index may be determined using the motion delta value d(t)and a motion history input value h(t−1) corresponding to the spatiallocation (j,i) from the previous frame, as shown in step 667.Additionally, though not shown, a motion history value h(t)corresponding to the current pixel x(t) may also be determined at step667 once the motion delta value d(t) is known, for example, by usingEquation 3a shown above. Thereafter, at step 668, a first filtercoefficient K may be selected from a motion table 655 using the motiontable lookup index from step 667. The determination of the motion tablelookup index and the selection of the first filter coefficient K fromthe motion table may be performed in accordance with Equation 2a, asshown above.

Next, at step 669, an attenuation factor may be selected from a lumatable 656. For instance, the luma table 656 may contain attenuationfactors ranging from between approximately 0 and 1, and the attenuationfactor may be selected from the luma table 656 using the value of thecurrent pixel x(t) as a lookup index. Once the attenuation factor isselected, a second filter coefficient K′ may be determined at step 670using the selected attenuation factor and the first filter coefficient K(from step 668), as shown in Equation 4a above. Then, at step 671, atemporally filtered output value y(t) corresponding to the current inputpixel x(t) is determined based upon the second filter coefficient K′(from step 669), the value of the collocated reference pixel r(t−1), andthe value of the input pixel x(t). For instance, in one embodiment, theoutput value y(t) may be determined in accordance with Equation 5a, asshown above.

Referring to FIG. 55, the step 666 for determining the motion deltavalue d(t) from the method 664 is illustrated in more detail inaccordance with one embodiment. In particular, the determination of themotion delta value d(t) may generally correspond to the operationdepicted above in accordance with Equation 1a. As shown, the step 666may include the sub-steps 672-675. Beginning at sub-step 672, a set ofthree horizontally adjacent pixels having the same color value as thecurrent input pixel x(t) are identified. By way of example, inaccordance with the embodiment shown in FIG. 53 the image data mayinclude Bayer image data, and the three horizontally adjacent pixels mayinclude the current input pixel x(t) (661), a second pixel 660 of thesame color to the left of the current input pixel 661, and a third pixelof the same color to the right of the current input pixel 661.

Next, at sub-step 673, three collocated reference pixels 657, 658, and659 from the previous frame corresponding to the selected set of threehorizontally adjacent pixels 660, 661, and 662 are identified. Using theselected pixels 660, 661, and 662 and the three collocated referencepixels 657, 658, and 659, the absolute values of the differences betweeneach of the three selected pixels 660, 661, and 662 and theircorresponding collocated reference pixels 657, 658, and 659,respectively, are determined at sub-step 674. Subsequently, at sub-step675, the maximum of the three differences from sub-step 674 is selectedas the motion delta value d(t) for the current input pixel x(t). Asdiscussed above, FIG. 55, which illustrates the motion delta valuecalculation technique shown in Equation 1a, is only intended to provideone embodiment. Indeed, as discussed above, any suitable two-dimensionalarray of same-colored pixels with the current pixel being centered inthe array may be used to determine a motion delta value (e.g., Equation1b).

Another embodiment of a technique for applying temporal filtering toimage data is further depicted in FIG. 56. For instance, since signal tonoise ratios for different color components of the image data may bedifferent, a gain may be applied to the current pixel, such that thecurrent pixel is gained before selecting motion and luma values from themotion table 655 and luma table 656. By applying a respective gain thatis color dependent, signal to noise ratio may be more consistent amongthe different color components. By way of example only, in animplementation that uses raw Bayer image data, the red and blue colorchannels may generally be more sensitive compared to the green (Gr andGb) color channels. Thus, by applying an appropriate color-dependentgain to each processed pixel, the signal to noise variation between eachcolor component may be generally reduced, thereby reducing, among otherthings, ghosting artifacts, as well as consistency across differentcolors after auto-white balance gains.

With this in mind, FIG. 56 provides a flow chart depicting a method 676for applying temporal filtering to image data received by the front-endprocessing unit 150 in accordance with such an embodiment. Beginning atstep 677, a current pixel x(t) located at spatial location (j,i) of acurrent frame of image data is received by the temporal filtering system654. At step 678, a motion delta value d(t) is determined for thecurrent pixel x(t) based at least partially upon one or more collocatedreference pixels (e.g., r(t−1)) from a previous frame of the image data(e.g., the image frame immediately preceding the current frame). Thestep 678 may be similar to the step 666 of FIG. 54, and may utilize theoperation represented in Equation 1 above.

Next, at step 679, a motion table lookup index may be determined usingthe motion delta value d(t), a motion history input value h(t−1)corresponding to the spatial location (j,i) from the previous frame(e.g., corresponding to the collocated reference pixel r(t−1)), and again associated with the color of the current pixel. Thereafter, at step680, a first filter coefficient K may be selected from the motion table655 using the motion table lookup index determined at step 679. By wayof example only, in one embodiment, the filter coefficient K and themotion table lookup index may be determined as follows:

K=M[gain[c]×(d(j,i,t)+h(j,i,t−1))],  (2b)

wherein M represents the motion table, and wherein the gain[c]corresponds to a gain associated with the color of the current pixel.Additionally, though not shown in FIG. 56, it should be understood thata motion history output value h(t) for the current pixel may also bedetermined and may be used to apply temporal filtering to a collocatedpixel of a subsequent image frame (e.g., the next frame). In the presentembodiment, the motion history output h(t) for the current pixel x(t)may be determined using the following formula:

h(j,i,t)=d(j,i,t)+K[h(j,i,t−1)−d(j,i,t)]  (3b)

Next, at step 681, an attenuation factor may be selected from the lumatable 656 using a luma table lookup index determined based upon the gain(gain[c]) associated with the color of the current pixel x(t). Asdiscussed above, the attenuation factors stored in the luma table mayhave a range from approximately 0 to 1. Thereafter, at step 682, asecond filter coefficient K′ may be calculated based upon theattenuation factor (from step 681) and the first filter coefficient K(from step 680). By way of example only, in one embodiment, the secondfilter coefficient K′ and the luma table lookup index may be determinedas follows:

K′=K×L[gain[c]×x(j,i,t)]  (4b)

Next, at step 683, a temporally filtered output value y(t) correspondingto the current input pixel x(t) is determined based upon the secondfilter coefficient K′ (from step 682), the value of the collocatedreference pixel r(t−1), and the value of the input pixel x(t). Forinstance, in one embodiment, the output value y(t) may be determined asfollows:

y(j,i,t)=x(j,i,t)+K′(r(j,i,t−1)−x(j,i,t))  (5b)

Continuing to FIG. 57, a further embodiment of the temporal filteringprocess 384 is depicted. Here, the temporal filtering process 384 may beaccomplished in a manner similar to the embodiment discussed in FIG. 56,except that instead of applying a color-dependent gain (e.g., gain[c])to each input pixel and using shared motion and luma tables, separatemotion and luma tables are provided for each color components. Forinstance, as shown in FIG. 57, the motion tables 655 may include amotion table 655 a corresponding to a first color, a motion table 655 bcorresponding to a second color, and a motion table 655 c correspondingto an nth color, wherein n depends on the number of colors present inthe raw image data. Similarly, the luma tables 656 may include a lumatable 656 a corresponding to the first color, a luma table 656 bcorresponding to the second color, and a luma table 656 c correspondingto the nth color. Thus, in an embodiment where the raw image data isBayer image data, three motion and luma tables may be provided for eachof the red, blue, and green color components. As discussed below, theselection of filtering coefficients K and attenuation factors may dependon the motion and luma table selected for the current color (e.g., thecolor of the current input pixel).

A method 685 illustrating a further embodiment for temporal filteringusing color-dependent motion and luma tables is shown in FIG. 58. Aswill be appreciated, the various calculations and formulas that may beemployed by the method 685 may be similar to the embodiment shown inFIG. 54, but with a particular motion and luma table being selected foreach color, or similar to the embodiment shown in FIG. 56, but replacingthe use of the color dependent gain[c] with the selection of acolor-dependent motion and luma table.

Beginning at step 686, a current pixel x(t) located at spatial location(j,i) of a current frame of image data is received by the temporalfiltering system 684 (FIG. 57). At step 687, a motion delta value d(t)is determined for the current pixel x(t) based at least partially uponone or more collocated reference pixels (e.g., r(t−1)) from a previousframe of the image data (e.g., the image frame immediately preceding thecurrent frame). Step 687 may be similar to the step 666 of FIG. 54, andmay utilize the operation shown in Equation 1 above.

Next, at step 688, a motion table lookup index may be determined usingthe motion delta value d(t) and a motion history input value h(t−1)corresponding to the spatial location (j,i) from the previous frame(e.g., corresponding to the collocated reference pixel r(t−1)).Thereafter, at step 689, a first filter coefficient K may be selectedfrom one of the available motion tables (e.g., 655 a, 655 b, 655 c)based upon the color of the current input pixel. For instance, one theappropriate motion table is identified, the first filter coefficient Kmay be selected using the motion table lookup index determined in step688.

After selecting the first filter coefficient K, a luma tablecorresponding to the current color is selected and an attenuation factoris selected from the selected luma table based upon the value of thecurrent pixel x(t), as shown at step 690. Thereafter, at step 691, asecond filter coefficient K′ is determined based upon the attenuationfactor (from step 690) and the first filter coefficient K (step 689).Next, at step 692, a temporally filtered output value y(t) correspondingto the current input pixel x(t) is determined based upon the secondfilter coefficient K′ (from step 691), the value of the collocatedreference pixel r(t−1), and the value of the input pixel x(t). While thetechnique shown in FIG. 58 may be more costly to implement (e.g., due tothe memory needed for storing additional motion and luma tables), itmay, in some instances, offer further improvements with regard toghosting artifacts and consistency across different colors afterauto-white balance gains.

In accordance with further embodiments, the temporal filtering processprovided by the temporal filter 650 may utilize a combination ofcolor-dependent gains and color-specific motion and/or luma tables forapplying temporal filtering to the input pixels. For instance, in onesuch embodiment, a single motion table may be provided for all colorcomponents, and the motion table lookup index for selecting the firstfiltering coefficient (K) from the motion table may be determined basedupon a color dependent gain (e.g., as shown in FIG. 56, steps 679-680),while the luma table lookup index may not have a color dependent gainapplied thereto, but may be used to select the brightness attenuationfactor from one of multiple luma tables depending upon the color of thecurrent input pixel (e.g., as shown in FIG. 58, step 690).Alternatively, in another embodiment, multiple motion tables may beprovided and a motion table lookup index (without a color dependent gainapplied) may be used to select the first filtering coefficient (K) froma motion table corresponding to the color of the current input pixel(e.g., as shown in FIG. 58, step 689), while a single luma table may beprovided for all color components, and wherein the luma table lookupindex for selecting the brightness attenuation factor may be determinedbased upon a color dependent gain (e.g., as shown in FIG. 56, steps681-682). Further, in one embodiment where a Bayer color filter array isutilized, one motion table and/or luma table may be provided for each ofthe red (R) and blue (B) color components, while a common motion tableand/or luma table may be provided for both green color components (Grand Gb).

The output of the temporal filter 650 may subsequently be sent to thebinning compensation filter (BCF) 652, which may be configured toprocess the image pixels to compensate for non-linear placement (e.g.,uneven spatial distribution) of the color samples due to binning by theimage sensor(s) 90 a or 90 b, such that subsequent image processingoperations in the ISP pipe logic 82 (e.g., demosaicing, etc.) thatdepend on linear placement of the color samples can operate correctly.For example, referring now to FIG. 59, a full resolution sample 693 ofBayer image data is depicted. This may represent a full resolutionsample raw image data captured by the image sensor 90 a (or 90 b)coupled to the ISP front-end processing logic 80.

As will be appreciated, under certain image capture conditions, it maybe not be practical to send the full resolution image data captured bythe image sensor 90 a to the ISP circuitry 32 for processing. Forinstance, when capturing video data, in order to preserve the appearanceof a fluid moving image from the perspective of the human eye, a framerate of at least approximately 30 frames per second may be desired.However, if the amount of pixel data contained in each frame of a fullresolution sample exceeds the processing capabilities of the ISPcircuitry 32 when sampled at 30 frames per second, binning compensationfiltering may be applied in conjunction with binning by the image sensor90 a to reduce the resolution of the image signal while also improvingsignal-to-noise ratio. For instance, as discussed above, various binningtechniques, such as 2×2 binning, may be applied to produce a “binned”raw image pixel by averaging the values of surrounding pixels in theactive region 312 of the raw frame 310.

Referring to FIG. 60, an embodiment of the image sensor 90 a that may beconfigured to bin the full resolution image data 693 of FIG. 59 toproduce corresponding binned raw image data 700 shown in FIG. 61 isillustrated in accordance with one embodiment. As shown, the imagesensor 90 a may capture the full resolution raw image data 693. Binninglogic 699 may be configured to apply binning to the full resolution rawimage data 693 to produce the binned raw image data 700, which may beprovided to the ISP front-end processing logic 80 using the sensorinterface 94 a which, as discussed above, may be an SMIA interface orany other suitable parallel or serial camera interfaces.

As illustrated in FIG. 61, the binning logic 699 may apply 2×2 binningto the full resolution raw image data 693. For example, with regard tothe binned image data 700, the pixels 695, 696, 697, and 698 may form aBayer pattern and may be determined by averaging the values of thepixels from the full resolution raw image data 693. For instance,referring to both FIGS. 59 and 61, the binned Gr pixel 695 may bedetermined as the average or mean of the full resolution Gr pixels 695a-695 d. Similarly, the binned R pixel 696 may be determined as theaverage of the full resolution R pixels 696 a-696 d, the binned B pixel697 may be determined as the average of the full resolution B pixels 697a-697 d, and the binned Gb pixel 698 may be determined as the average ofthe full resolution Gb pixels 698 a-698 d. Thus, in the presentembodiment, 2×2 binning may provide a set of four full resolution pixelsincluding an upper left (e.g., 695 a), upper right (e.g., 695 b), lowerleft (e.g., 695 c), and lower right (e.g., 695 d) pixel that areaveraged to derive a binned pixel located at the center of a squareformed by the set of four full resolution pixels. Accordingly, thebinned Bayer block 694 shown in FIG. 61 contains four “superpixels” thatrepresent the 16 pixels contained in the Bayer blocks 694 a-694 d ofFIG. 59.

In addition to reducing spatial resolution, binning also offers theadded advantage of reducing noise in the image signal. For instance,whenever an image sensor (e.g., 90 a) is exposed to a light signal,there may be a certain amount of noise, such as photon noise, associatedwith the image. This noise may be random or systematic and it also maycome from multiple sources. Thus, the amount of information contained inan image captured by the image sensor may be expressed in terms of asignal-to-noise ratio. For example, every time an image is captured byan image sensor 90 a and transferred to a processing circuit, such asthe ISP circuitry 32, there may be some degree of noise in the pixelsvalues because the process of reading and transferring the image datainherently introduces “read noise” into the image signal. This “readnoise” may be random and is generally unavoidable. By using the averageof four pixels, noise, (e.g., photon noise) may generally be reducedirrespective of the source of the noise.

Thus, when considering the full resolution image data 693 of FIG. 59,each Bayer pattern (2×2 block) 694 a-694 d contains 4 pixels, each ofwhich contains a signal and noise component. If each pixel in, forexample, the Bayer block 694 a, is read separately, then four signalcomponents and four noise components are present. However, by applyingbinning, as shown in FIGS. 59 and 61, such that four pixels (e.g., 695a, 695 b, 695 c, 695 d) may be represented by a single pixel (e.g., 695)in the binned image data, the same area occupied by the four pixels inthe full resolution image data 693 may be read as a single pixel withonly one instance of a noise component, thus improving signal-to-noiseratio.

Further, while the present embodiment depicts the binning logic 699 ofFIG. 60 as being configured to apply a 2×2 binning process, it should beappreciated that the binning logic 699 may be configured to apply anysuitable type of binning process, such as 3×3 binning, vertical binning,horizontal binning, and so forth. In some embodiments, the image sensor90 a may be configured to select between different binning modes duringthe image capture process. Additionally, in further embodiments, theimage sensor 90 a may also be configured to apply a technique that maybe referred to as “skipping,” wherein instead of average pixel samples,the logic 699 selects only certain pixels from the full resolution data693 (e.g., every other pixel, every 3 pixels, etc.) to output to the ISPfront-end 80 for processing. Further, while only the image sensor 90 ais shown in FIG. 60, it should be appreciated that the image sensor 90 bmay be implemented in a similar manner.

As also depicted in FIG. 61, one effect of the binning process is thatthe spatial sampling of the binned pixels may not be equally spaced.This spatial distortion may, in some systems, result in aliasing (e.g.,jagged edges), which is generally not desirable. Further, becausecertain image processing steps in the ISP pipe logic 82 may depend uponon the linear placement of the color samples in order to operatecorrectly, the binning compensation filter (BCF) 652 may be applied toperform re-sampling and re-positioning of the binned pixels such thatthe binned pixels are spatially evenly distributed. That is, the BCF 652essentially compensates for the uneven spatial distribution (e.g., shownin FIG. 61) by re-sampling the position of the samples (e.g., pixels).For instance, FIG. 62 illustrates a re-sampled portion of binned imagedata 702 after being processed by the BCF 652, wherein the Bayer block703 containing the evenly distributed re-sampled pixels 704, 705, 706,and 707 correspond to the binned pixels 695, 696, 697, and 698,respectively, of the binned image data 700 from FIG. 61. Additionally,in an embodiment that utilizes skipping (e.g., instead of binning), asmentioned above, the spatial distortion shown in FIG. 61 may not bepresent. In this case, the BCF 652 may function as a low pass filter toreduce artifacts (e.g., aliasing) that may result when skipping isemployed by the image sensor 90 a.

FIG. 63 shows a block diagram of the binning compensation filter 652 inaccordance with one embodiment. The BCF 652 may include binningcompensation logic 708 that may process binned pixels 700 to applyhorizontal and vertical scaling using horizontal scaling logic 709 andvertical scaling logic 710, respectively, to re-sample and re-positionthe binned pixels 700 so that they are arranged in a spatially evendistribution, as shown in FIG. 62. In one embodiment, the scalingoperation(s) performed by the BCF 652 may be performed using horizontaland vertical multi-tap polyphase filtering. For instance, the filteringprocess may include selecting the appropriate pixels from the inputsource image data (e.g., the binned image data 700 provided by the imagesensor 90 a), multiplying each of the selected pixels by a filteringcoefficient, and summing up the resulting values to form an output pixelat a desired destination.

The selection of the pixels used in the scaling operations, which mayinclude a center pixel and surrounding neighbor pixels of the samecolor, may be determined using separate differential analyzers 711, onefor vertical scaling and one for horizontal scaling. In the depictedembodiment, the differential analyzers 711 may be digital differentialanalyzers (DDAs) and may be configured to control the current outputpixel position during the scaling operations in the vertical andhorizontal directions. In the present embodiment, a first DDA (referredto as 711 a) is used for all color components during horizontal scaling,and a second DDA (referred to as 711 b) is used for all color componentsduring vertical scaling. By way of example only, the DDA 711 may beprovided as a 32-bit data register that contains a 2's-complementfixed-point number having 16 bits in the integer portion and 16 bits inthe fraction. The 16-bit integer portion may be used to determine thecurrent position for an output pixel. The fractional portion of the DDA711 may be used to determine a current index or phase, which may bebased the between-pixel fractional position of the current DDA position(e.g., corresponding to the spatial location of the output pixel). Theindex or phase may be used to select an appropriate set of coefficientsfrom a set of filter coefficient tables 712. Additionally, the filteringmay be done per color component using same colored pixels. Thus, thefiltering coefficients may be selected based not only on the phase ofthe current DDA position, but also the color of the current pixel. Inone embodiment, 8 phases may be present between each input pixel and,thus, the vertical and horizontal scaling components may utilize 8-deepcoefficient tables, such that the high-order 3 bits of the 16-bitfraction portion are used to express the current phase or index. Thus,as used herein, the term “raw image” data or the like shall beunderstood to refer to multi-color image data that is acquired by asingle sensor with a color filter array pattern (e.g., Bayer) overlayingit, those providing multiple color components in one plane. In anotherembodiment, separate DDAs may be used for each color component. Forinstance, in such embodiments, the BCF 652 may extract the R, B, Gr, andGb components from the raw image data and process each component as aseparate plane.

In operation, horizontal and vertical scaling may include initializingthe DDA 711 and performing the multi-tap polyphase filtering using theinteger and fractional portions of the DDA 711. While performedseparately and with separate DDAs, the horizontal and vertical scalingoperations are carried out in a similar manner. A step value or stepsize (DDAStepX for horizontal scaling and DDAStepY for vertical scaling)determines how much the DDA value (currDDA) is incremented after eachoutput pixel is determined, and multi-tap polyphase filtering isrepeated using the next currDDA value. For instance, if the step valueis less than 1, then the image is up-scaled, and if the step value isgreater than 1, the image is downscaled. If the step value is equal to1, then no scaling occurs. Further, it should be noted that same ordifferent step sizes may be used for horizontal and vertical scaling.

Output pixels are generated by the BCF 652 in the same order as inputpixels (e.g., using the Bayer pattern). In the present embodiment, theinput pixels may be classified as being even or odd based on theirordering. For instance, referring to FIG. 64, a graphical depiction ofinput pixel locations (row 713) and corresponding output pixel locationsbased on various DDAStep values (rows 714-718) are illustrated. In thisexample, the depicted row represents a row of red (R) and green (Gr)pixels in the raw Bayer image data. For horizontal filtering purposes,the red pixel at position 0.0 in the row 713 may be considered an evenpixel, the green pixel at position 1.0 in the row 713 may be consideredan odd pixel, and so forth. For the output pixel locations, even and oddpixels may be determined based on the least significant bit in thefraction portion (lower 16 bits) of the DDA 711. For instance, assuminga DDAStep of 1.25, as shown in row 715, the least significant bitcorresponds to the bit 14 of the DDA, as this bit gives a resolution of0.25. Thus, the red output pixel at the DDA position (currDDA) 0.0 maybe considered an even pixel (the least significant bit, bit 14, is 0),the green output pixel at currDDA 1.0 (bit 14 is 1), and so forth.Further, while FIG. 64 is discussed with respect to filtering in thehorizontal direction (using DDAStepX), it should be understood that thedetermination of even and odd input and output pixels may be applied inthe same manner with respect to vertical filtering (using DDAStepY). Inother embodiments, the DDAs 711 may also be used to track locations ofthe input pixels (e.g., rather than track the desired output pixellocations). Further, it should be appreciated that DDAStepX and DDAStepYmay be set to the same or different values. Further, assuming a Bayerpattern is used, it should be noted that the starting pixel used by theBCF 652 could be any one of a Gr, Gb, R, or B pixel depending, forinstance, on which pixel is located at a corner within the active region312.

With this in mind, the even/odd input pixels are used to generate theeven/odd output pixels, respectively. Given an output pixel locationalternating between even and odd position, a center source input pixellocation (referred to herein as “currPixel”) for filtering purposes isdetermined by the rounding the DDA to the closest even or odd inputpixel location for even or odd output pixel locations (based onDDAStepX), respectively. In an embodiment where the DDA 711 a isconfigured to use 16 bits to represent an integer and 16 bits torepresent a fraction, currPixel may be determined for even and oddcurrDDA positions using Equations 6a and 6b below:

Even output pixel locations may be determined based on bits [31:16] of:

(currDDA+1.0)&0xFFFE.0000  (6a)

Odd output pixel locations may be determined based on bits [31:16] of:

(currDDA)|0x0001.0000  (6b)

Essentially, the above equations present a rounding operation, wherebythe even and odd output pixel positions, as determined by currDDA, arerounded to the nearest even and odd input pixel positions, respectively,for the selection of currPixel.

Additionally, a current index or phase (currindex) may also bedetermined at each currDDA position. As discussed above, the index orphase values represent the fractional between-pixel position of theoutput pixel position relative to the input pixel positions. Forinstance, in one embodiment, 8 phases may be defined between each inputpixel position. For instance, referring again to FIG. 64, 8 index values0-7 are provided between the first red input pixel at position 0.0 andthe next red input pixel at position 2.0. Similarly, 8 index values 0-7are provided between the first green input pixel at position 1.0 and thenext green input pixel at position 3.0. In one embodiment, the currindexvalues may be determined in accordance with Equations 7a and 7b belowfor even and odd output pixel locations, respectively:

Even output pixel locations may be determined based on bits [16:14] of:

(currDDA+0.125)  (7a)

Odd output pixel locations may be determined based on bits [16:14] of:

(currDDA+1.125)  (7b)

For the odd positions, the additional 1 pixel shift is equivalent toadding an offset of four to the coefficient index for odd output pixellocations to account for the index offset between different colorcomponents with respect to the DDA 711.

Once currPixel and currindex have been determined at a particularcurrDDA location, the filtering process may select one or moreneighboring same-colored pixels based on currPixel (the selected centerinput pixel). By way of example, in an embodiment where the horizontalscaling logic 709 includes a 5-tap polyphase filter and the verticalscaling logic 710 includes a 3-tap polyphase filter, two same-coloredpixels on each side of currPixel in the horizontal direction may beselected for horizontal filtering (e.g., −2, −1, 0, +1, +2), and onesame-colored pixel on each side of currPixel in the vertical directionmay be selected for vertical filtering (e.g., −1, 0, +1). Further,currindex may be used as a selection index to select the appropriatefiltering coefficients from the filter coefficients table 712 to applyto the selected pixels. For instance, using the 5-tap horizontal/3-tapvertical filtering embodiment, five 8-deep tables may be provided forhorizontal filtering, and three 8-deep tables may be provided forvertical filtering. Though illustrated as part of the BCF 652, it shouldbe appreciated that the filter coefficient tables 712 may, in certainembodiments, be stored in a memory that is physically separate from theBCF 652, such as the memory 108.

Before discussing the horizontal and vertical scaling operations infurther detail, Table 5 below shows examples of how currPixel andcurrIndex values, as determined based on various DDA positions usingdifferent DDAStep values (e.g., could apply to DDAStepX or DDAStepY).

TABLE 5 Binning Compensation Filter - DDA Examples of currPixel andcurrIndex calculation Output DDA DDA DDA DDA Pixel Step 1.25 Step 1.5Step 1.75 Step 2.0 (Even or curr curr curr curr curr curr curr curr currcurr curr curr Odd) DDA Index Pixel DDA Index Pixel DDA Index Pixel DDAIndex Pixel 0 0.0 0 0 0.0 0 0 0.0 0 0 0.0 0 0 1 1.25 1 1 1.5 2 1 1.75 31 2 4 3 0 2.5 2 2 3 4 4 3.5 6 4 4 0 4 1 3.75 3 3 4.5 6 5 5.25 1 5 6 4 70 5 4 6 6 0 6 7 4 8 8 0 8 1 6.25 5 7 7.5 2 7 8.75 7 9 10 4 11 0 7.5 6 89 4 10 10.5 2 10 12 0 12 1 8.75 7 9 10.5 6 11 12.25 5 13 14 4 15 0 10 010 12 0 12 14 0 14 16 0 16 1 11.25 1 11 13.5 2 13 15.75 3 15 18 4 19 012.5 2 12 15 4 16 17.5 6 18 20 0 20 1 13.75 3 13 16.5 6 17 19.25 1 19 224 23 0 15 4 16 18 0 18 21 4 22 24 0 24 1 16.25 5 17 19.5 2 19 22.75 7 2326 4 27 0 17.5 6 18 21 4 22 24.5 2 24 28 0 28 1 18.75 7 19 22.5 6 2326.25 5 27 30 4 31 0 20 0 20 24 0 24 28 0 28 32 0 32

To provide an example, let us assume that a DDA step size (DDAStep) of1.5 is selected (row 716 of FIG. 64), with the current DDA position(currDDA) beginning at 0, indicating an even output pixel position. Todetermine currPixel, Equation 6a may be applied, as shown below:

  currDDA = 0.0  (even)       0000  0000  0000  0001.0000  0000  0000  0000  (currDDA + 1.0)${({AND})\mspace{31mu} 1111\mspace{14mu} 1111\mspace{14mu} 1111\mspace{14mu} 1110.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} \left( {0x\; {FFFE}{.0000}} \right)}\mspace{65mu} = \; {\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}{.0000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}$  currPixel  (determined  as  bits  [31∷16]  of  the  result) = 0;

Thus, at the currDDA position 0.0 (row 716), the source input centerpixel for filtering corresponds to the red input pixel at position 0.0of row 713.

To determine currIndex at the even currDDA 0.0, Equation 7a may beapplied, as shown below:

currDDA = 0.0  (even)   $\begin{matrix}\; & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & ({currDDA}) \\ + & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & (0.125) \\ = & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 000\underset{\_}{0.00}10\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \;\end{matrix}$currIndex  (determined  as  bits  [16:14]  of  the  result) = [000] = 0;

Thus, at the currDDA position 0.0 (row 716), a currIndex value of 0 maybe used to select filtering coefficients from the filter coefficientstable 712.

Accordingly, filtering (which may be vertical or horizontal depending onwhether DDAStep is in the X (horizontal) or Y (vertical) direction) mayapplied based on the determined currPixel and currIndex values atcurrDDA 0.0, and the DDA 711 is incremented by DDAStep (1.5), and thenext currPixel and currIndex values are determined. For instance, at thenext currDDA position 1.5 (an odd position), currPixel may be determinedusing Equation 6b as follows:

  currDDA = 0.0  (odd) $\begin{matrix}\; & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.1000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & {({currDDA})\mspace{14mu}} \\{\; ({OR})} & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \left( {0x\; 0001.0000} \right) \\ = & {\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001}{.1000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \;\end{matrix}$  currPixel  (determined  as  bits  [31∷16]  of  the  result) = 1;

Thus, at the currDDA position 1.5 (row 716), the source input centerpixel for filtering corresponds to the green input pixel at position 1.0of row 713.

Further, currIndex at the odd currDDA 1.5 may be determined usingEquation 7b, as shown below:

currDDA = 1.5  (odd) $\begin{matrix}\; & {{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.1000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{11mu}} & ({currDDA}) \\ + & {{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{11mu}} & (1.125) \\ = & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 001\underset{\_}{0.10}10\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \;\end{matrix}$currIndex  (determined  as  bits  [16:14]  of  the  result) = [010] = 2;

Thus, at the currDDA position 1.5 (row 716), a currIndex value of 2 maybe used to select the appropriate filtering coefficients from the filtercoefficients table 712. Filtering (which may be vertical or horizontaldepending on whether DDAStep is in the X (horizontal) or Y (vertical)direction) may thus be applied using these currPixel and currIndexvalues.

Next, the DDA 711 is incremented again by DDAStep (1.5), resulting in acurrDDA value of 3.0. The currPixel corresponding to currDDA 3.0 may bedetermined using Equation 6a, as shown below:

  currDDA = 3.0  (even) $\begin{matrix}\; & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0100.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \left( {{currDDA} + 1.0} \right) \\({AND}) & {1111\mspace{14mu} 1111\mspace{14mu} 1111\mspace{14mu} 1110.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \left( {0x\; {FFFE}{.0000}} \right) \\ = & {\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0100}{.0000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \;\end{matrix}$  currPixel  (determined  as  bits  [36∷16]  of  the  result) = 4

Thus, at the currDDA position 3.0 (row 716), the source input centerpixel for filtering corresponds to the red input pixel at position 4.0of row 713.

Next, currIndex at the even currDDA 3.0 may be determined using Equation7a, as shown below:

currDDA = 3.0  (even) $\begin{matrix}\; & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0011.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & ({currDDA}) \\ + & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & (0.125) \\ = & {0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0011.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000} & \;\end{matrix}$currIndex  (determined  as  bits  [16:14]  of  the  result) = [100] = 4;

Thus, at the currDDA position 3.0 (row 716), a currIndex value of 4 maybe used to select the appropriate filtering coefficients from the filtercoefficients table 712. As will be appreciated, the DDA 711 may continueto be incremented by DDAStep for each output pixel, and filtering (whichmay be vertical or horizontal depending on whether DDAStep is in the X(horizontal) or Y (vertical) direction) may be applied using thecurrPixel and currIndex determined for each currDDA value.

As discussed above, currIndex may be used as a selection index to selectthe appropriate filtering coefficients from the filter coefficientstable 712 to apply to the selected pixels. The filtering process mayinclude obtaining the source pixel values around the center pixel(currPixel), multiplying each of the selected pixels by the appropriatefiltering coefficients selected from the filter coefficients table 712based on currIndex, and summing the results to obtain a value of theoutput pixel at the location corresponding to currDDA. Further, becausethe present embodiment utilizes 8 phases between same colored pixels,using the 5-tap horizontal/3-tap vertical filtering embodiment, five8-deep tables may be provided for horizontal filtering, and three 8-deeptables may be provided for vertical filtering. In one embodiment, eachof the coefficient table entries may include a 16-bit 2's complementfixed point number with 3 integer bits and 13 fraction bits.

Further, assuming a Bayer image pattern, in one embodiment, the verticalscaling component may include four separate 3-tap polyphase filters, onefor each color component: Gr, R, B, and Gb. Each of the 3-tap filtersmay use the DDA 711 to control the stepping of the current center pixeland the index for the coefficients, as described above. Similarly, thehorizontal scaling components may include four separate 5-tap polyphasefilters, one for each color component: Gr, R, B, and Gb. Each of the5-tap filters may use the DDA 711 to control the stepping (e.g., viaDDAStep) of the current center pixel and the index for the coefficients.It should be understood however, that fewer or more taps could beutilized by the horizontal and vertical scalars in other embodiments.

For boundary cases, the pixels used in the horizontal and verticalfiltering process may depend upon the relationship of the current DDAposition (currDDA) relative to a frame border (e.g., border defined bythe active region 312 in FIG. 23). For instance, in horizontalfiltering, if the currDDA position, when compared to the position of thecenter input pixel (SrcX) and the width (SrcWidth) of the frame (e.g.,width 322 of the active region 312 of FIG. 23) indicates that the DDA711 is close to the border such that there are not enough pixels toperform the 5-tap filtering, then the same-colored input border pixelsmay be repeated. For instance, if the selected center input pixel is atthe left edge of the frame, then the center pixel may be replicatedtwice for horizontal filtering. If the center input pixel is near theleft edge of the frame such that only one pixel is available between thecenter input pixel and the left edge, then, for horizontal filteringpurposes, the one available pixel is replicated in order to provide twopixel values to the left of the center input pixel. Further, thehorizontal scaling logic 709 may be configured such that the number ofinput pixels (including original and replicated pixels) cannot exceedthe input width. This may be expressed as follows:

StartX=(((DDAInitX+0x0001.0000)&0xFFFE.0000)>>16)

EndX=(((DDAInitX+DDAStepX*(BCFOutWidth−1))|0x0001.0000)>>16)

EndX−StartX<=SrcWidth−1

wherein, DDAInitX represents the initial position of the DDA 711,DDAStepX represents the DDA step value in the horizontal direction, andBCFOutWidth represents the width of the frame output by the BCF 652.

For vertical filtering, if the currDDA position, when compared to theposition of the center input pixel (SrcY) and the width (SrcHeight) ofthe frame (e.g., width 322 of the active region 312 of FIG. 23)indicates that the DDA 711 is close to the border such that there arenot enough pixels to perform the 3-tap filtering, then the input borderpixels may be repeated. Further, the vertical scaling logic 710 may beconfigured such that the number of input pixels (including original andreplicated pixels) cannot exceed the input height. This may be expressedas follows:

StartY=(((DDAInitY+0x0001.0000)&0xFFFE.0000)>>16)

EndY=(((DDAInitY+DDAStepY*(BCFOutHeight−1))10x0001.0000)>>16)

EndY−StartY<=SrcHeight−1

wherein, DDAInitY represents the initial position of the DDA 711,DDAStepY represents the DDA step value in the vertical direction, andBCFOutHeight represents the width of the frame output by the BCF 652.

Referring now to FIG. 65, a flow chart depicting a method 720 forapplying binning compensation filtering to image data received by thefront-end pixel processing unit 150 in accordance with an embodiment. Itwill be appreciated that the method 720 illustrated in FIG. 65 may applyto both vertical and horizontal scaling. Beginning at step 721 the DDA711 is initialized and a DDA step value (which may correspond toDDAStepX for horizontal scaling and DDAStepY for vertical scaling) isdetermined. Next, at step 722, a current DDA position (currDDA), basedon DDAStep, is determined. As discussed above, currDDA may correspond toan output pixel location. Using currDDA, the method 720 may determine acenter pixel (currPixel) from the input pixel data that may be used forbinning compensation filtering to determine a corresponding output valueat currDDA, as indicated at step 723. Subsequently, at step 724, anindex corresponding to currDDA (currIndex) may be determined based onthe fractional between-pixel position of currDDA relative to the inputpixels (e.g., row 713 of FIG. 64). By way of example, in an embodimentwhere the DDA includes 16 integer bits and 16 fraction bits, currPixelmay be determined in accordance with Equations 6a and 6b, and currIndexmay be determined in accordance with Equations 7a and 7b, as shownabove. While the 16 bit integer/16 bit fraction configuration isdescribed herein as one example, it should be appreciated that otherconfigurations of the DDA 711 may be utilized in accordance with thepresent technique. By way of example, other embodiments of the DDA 711may be configured to include a 12 bit integer portion and 20 bitfraction portion, a 14 bit integer portion and 18 bit fraction portion,and so forth.

Once currPixel and currIndex are determined, same-colored source pixelsaround currPixel may be selected for multi-tap filtering, as indicatedby step 725. For instance, as discussed above, one embodiment mayutilize 5-tap polyphase filtering in the horizontal direction (e.g.,selecting 2 same-colored pixels on each side of currPixel) and mayutilize 3-tap polyphase filtering in the vertical direction (e.g.,selecting 1 same-colored pixel on each side of currPixel). Next, at step726, once the source pixels are selected, filtering coefficients may beselected from the filter coefficients table 712 of the BCF 652 basedupon currIndex.

Thereafter, at step 727, filtering may be applied to the source pixelsto determine the value of an output pixel corresponding to the positionrepresented by currDDA. For instance, in one embodiment, the sourcepixels may be multiplied by their respective filtering coefficients, andthe results may be summed to obtain the output pixel value. Thedirection in which filtering is applied at step 727 may be vertical orhorizontal depending on whether DDAStep is in the X (horizontal) or Y(vertical) direction. Finally, at step 263, the DDA 711 is incrementedby DDAStep at step 728, and the method 720 returns to step 722, wherebythe next output pixel value is determined using the binning compensationfiltering techniques discussed herein.

Referring to FIG. 66, the step 723 for determining currPixel from themethod 720 is illustrated in more detail in accordance with oneembodiment. For instance, step 723 may include the sub-step 729 ofdetermining whether the output pixel location corresponding to currDDA(from step 722) is even or odd. As discussed above, an even or oddoutput pixel may be determined based on the least significant bit ofcurrDDA based on DDAStep. For instance, given a DDAStep of 1.25, acurrDDA value of 1.25 may be determined as odd, since the leastsignificant bit (corresponding to bit 14 of the fractional portion ofthe DDA 711) has a value of 1. For a currDDA value of 2.5, bit 14 is 0,thus indicating an even output pixel location.

At decision logic 730, a determination is made as to whether the outputpixel location corresponding to currDDA is even or odd. If the outputpixel is even, decision logic 730 continues to sub-step 731, whereincurrPixel is determined by incrementing the currDDA value by 1 androunding the result to the nearest even input pixel location, asrepresented by Equation 6a above. If the output pixel is odd, thendecision logic 730 continues to sub-step 732, wherein currPixel isdetermined by rounding the currDDA value to the nearest odd input pixellocation, as represented by Equation 6b above. The currPixel value maythen be applied to step 725 of the method 720 to select source pixelsfor filtering, as discussed above.

Referring also to FIG. 67, the step 724 for determining currIndex fromthe method 720 is illustrated in more detail in accordance with oneembodiment. For instance, step 724 may include the sub-step 733 ofdetermining whether the output pixel location corresponding to currDDA(from step 722) is even or odd. This determination may be performed in asimilar manner as step 729 of FIG. 66. At decision logic 734, adetermination is made as to whether the output pixel locationcorresponding to currDDA is even or odd. If the output pixel is even,decision logic 734 continues to sub-step 735, wherein currIndex isdetermined by incrementing the currDDA value by one index stepdetermining currIndex based on the lowest order integer bit and the twohighest order fraction bits of the DDA 711. For instance, in anembodiment wherein 8 phases are provided between each same-coloredpixel, and wherein the DDA includes 16 integer bits and 16 fractionbits, one index step may correspond to 0.125, and currIndex may bedetermined based on bits [16:14] of the currDDA value incremented by0.125 (e.g., Equation 7a). If the output pixel is odd, decision logic734 continues to sub-step 736, wherein currIndex is determined byincrementing the currDDA value by one index step and one pixel shift,and determining currIndex based on the lowest order integer bit and thetwo highest order fraction bits of the DDA 711. Thus, in an embodimentwherein 8 phases are provided between each same-colored pixel, andwherein the DDA includes 16 integer bits and 16 fraction bits, one indexstep may correspond to 0.125, one pixel shift may correspond to 1.0 (ashift of 8 index steps to the next same colored pixel), and currIndexmay be determined based on bits [16:14] of the currDDA value incrementedby 1.125 (e.g., Equation 7b).

While the presently illustrated embodiment provides the BCF 652 as acomponent of the front-end pixel processing unit 150, other embodimentsmay incorporate the BCF 652 into a raw image data processing pipeline ofthe ISP pipe 82 which, as discussed further below, may include defectivepixel detection/correction logic, gain/offset/compensation blocks, noisereduction logic, lens shading correction logic, and demosaicing logic.Further, in embodiments where the aforementioned defective pixeldetection/correction logic, gain/offset/compensation blocks, noisereduction logic, lens shading correction logic do not rely upon thelinear placement of the pixels, the BCF 652 may be incorporated with thedemosaicing logic to perform binning compensation filtering andreposition the pixels prior to demoasicing, as demosaicing generallydoes rely upon the even spatial positioning of the pixels. For instance,in one embodiment, the BCF 652 may be incorporated anywhere between thesensor input and the demosaicing logic, with temporal filtering and/ordefective pixel detection/correction being applied to the raw image dataprior to binning compensation.

As discussed above the output of the BCF 652, which may be the outputFEProcOut (109) having spatially evenly distributed image data (e.g.,sample 702 of FIG. 62), may be forwarded to the ISP pipe processinglogic 82 for additional processing. However, before shifting the focusof this discussion to the ISP pipe processing logic 82, a more detaileddescription of various functionalities that may be provided by thestatistics processing units (e.g., 142 and 144) that may be implementedin the ISP front-end logic 80 will first be provided.

Referring back to the general description of the statistics processingunits 142 and 144, these units may be configured to collect variousstatistics about the image sensors that capture and provide the rawimage signals (Sif0 and Sin), such as statistics relating toauto-exposure, auto-white balance, auto-focus, flicker detection, blacklevel compensation, and lens shading correction, and so forth. In doingso, the statistics processing units 142 and 144 may first apply one ormore image processing operations to their respective input signals, Sif0(from Sensor0) and Sif1 (from Sensor1).

For example, referring to FIG. 68, a more detailed block diagram view ofthe statistics processing unit 142 associated with Sensor0 (90 a) isillustrated in accordance with one embodiment. As shown, the statisticsprocessing unit 142 may include the following functional blocks:defective pixel detection and correction logic 738, black levelcompensation (BLC) logic 739, lens shading correction logic 740, inverseBLC logic 741, and statistics collection logic 742. Each of thesefunctional blocks will be discussed below. Further, it should beunderstood that the statistics processing unit 144 associated withSensor1 (90 b) may be implemented in a similar manner.

Initially, the output of selection logic 146 (e.g., Sif0 or SifIn0) isreceived by the front-end defective pixel correction logic 738. As willbe appreciated, “defective pixels” may be understood to refer to imagingpixels within the image sensor(s) 90 that fail to sense light levelsaccurately. Defective pixels may attributable to a number of factors,and may include “hot” (or leaky) pixels, “stuck” pixels, and “deadpixels.” A “hot” pixel generally appears as being brighter than anon-defective pixel given the same amount of light at the same spatiallocation. Hot pixels may result due to reset failures and/or highleakage. For example, a hot pixel may exhibit a higher than normalcharge leakage relative to non-defective pixels, and thus may appearbrighter than non-defective pixels. Additionally, “dead” and “stuck”pixels may be the result of impurities, such as dust or other tracematerials, contaminating the image sensor during the fabrication and/orassembly process, which may cause certain defective pixels to be darkeror brighter than a non-defective pixel, or may cause a defective pixelto be fixed at a particular value regardless of the amount of light towhich it is actually exposed. Additionally, dead and stuck pixels mayalso result from circuit failures that occur during operation of theimage sensor. By way of example, a stuck pixel may appear as alwaysbeing on (e.g., fully charged) and thus appears brighter, whereas a deadpixel appears as always being off.

The defective pixel detection and correction (DPDC) logic 738 in the ISPfront-end logic 80 may correct (e.g., replace defective pixel values)defective pixels before they are considered in statistics collection(e.g., 742). In one embodiment, defective pixel correction is performedindependently for each color component (e.g., R, B, Gr, and Gb for aBayer pattern). Generally, the front-end DPDC logic 738 may provide fordynamic defect correction, wherein the locations of defective pixels aredetermined automatically based upon directional gradients computed usingneighboring pixels of the same color. As will be understand, the defectsmay be “dynamic” in the sense that the characterization of a pixel asbeing defective at a given time may depend on the image data in theneighboring pixels. By way of example, a stuck pixel that is always onmaximum brightness may not be regarded as a defective pixel if thelocation of the stuck pixel is in an area of the current image that isdominate by brighter or white colors. Conversely, if the stuck pixel isin a region of the current image that is dominated by black or darkercolors, then the stuck pixel may be identified as a defective pixelduring processing by the DPDC logic 738 and corrected accordingly.

The DPDC logic 738 may utilize one or more horizontal neighboring pixelsof the same color on each side of a current pixel to determine if thecurrent pixel is defective using pixel-to-pixel directional gradients.If a current pixel is identified as being defective, the value of thedefective pixel may be replaced with the value of a horizontalneighboring pixel. For instance, in one embodiment, five horizontalneighboring pixels of the same color that are inside the raw frame 310(FIG. 23) boundary are used, wherein the five horizontal neighboringpixels include the current pixel and two neighboring pixels on eitherside. Thus, as illustrated in FIG. 69, for a given color component c andfor the current pixel P, horizontal neighbor pixels P0, P1, P2, and P3may be considered by the DPDC logic 738. It should be noted, however,that depending on the location of the current pixel P, pixels outsidethe raw frame 310 are not considered when calculating pixel-to-pixelgradients.

For instance, as shown in FIG. 69, in a “left edge” case 743, thecurrent pixel P is at the leftmost edge of the raw frame 310 and, thus,the neighboring pixels P0 and P1 outside of the raw frame 310 are notconsidered, leaving only the pixels P, P2, and P3 (N=3). In a “leftedge+1” case 744, the current pixel P is one unit pixel away from theleftmost edge of the raw frame 310 and, thus, the pixel P0 is notconsidered. This leaves only the pixels P1, P, P2, and P3 (N=4).Further, in a “centered” case 745, pixels P0 and P1 on the left side ofthe current pixel P and pixels P2 and P3 on the right side of thecurrent pixel P are within the raw frame 310 boundary and, therefore,all of the neighboring pixels P0, P1, P2, and P3 (N=5) are considered incalculating pixel-to-pixel gradients. Additionally, similar cases 746and 747 may be encountered as the rightmost edge of the raw frame 310 isapproached. For instance, given the “right edge-1” case 746, the currentpixel P is one unit pixel away the rightmost edge of the raw frame 310and, thus, the pixel P3 is not considered (N=4). Similarly, in the“right edge” case 747, the current pixel P is at the rightmost edge ofthe raw frame 310 and, thus, both of the neighboring pixels P2 and P3are not considered (N=3).

In the illustrated embodiment, for each neighboring pixel (k=0 to 3)within the picture boundary (e.g., raw frame 310), the pixel-to-pixelgradients may be calculated as follows:

G _(k)=abs(P−P _(k)),for 0≦k≦3(only for k within the raw frame)  (8)

Once the pixel-to-pixel gradients have been determined, defective pixeldetection may be performed by the DPDC logic 738 as follows. First, itis assumed that a pixel is defective if a certain number of itsgradients G_(k) are at or below a particular threshold, denoted by thevariable dprTh. Thus, for each pixel, a count (C) of the number ofgradients for neighboring pixels inside the picture boundaries that areat or below the threshold dprTh is accumulated. By way of example, foreach neighbor pixel inside the raw frame 310, the accumulated count C ofthe gradients G_(k) that are at or below the threshold dprTh may becomputed as follows:

$\begin{matrix}{{C = {\sum\limits_{k}^{N}\mspace{14mu} \left( {G_{k} \leq {dprTh}} \right)}},{{{for}\mspace{14mu} 0} \leq k \leq {3\mspace{14mu} \left( {{only}\mspace{14mu} {for}\mspace{14mu} k\mspace{14mu} {within}\mspace{14mu} {the}\mspace{14mu} {raw}\mspace{14mu} {frame}} \right)}}} & (9)\end{matrix}$

As will be appreciated, depending on the color components, the thresholdvalue dprTh may vary. Next, if the accumulated count C is determined tobe less than or equal to a maximum count, denoted by the variabledprMaxC, then the pixel may be considered defective. This logic isexpressed below:

if(C≦dprMaxC),then the pixel is defective.  (10)

Defective pixels are replaced using a number of replacement conventions.For instance, in one embodiment, a defective pixel may be replaced withthe pixel to its immediate left, P1. At a boundary condition (e.g., P1is outside of the raw frame 310), a defective pixel may replaced withthe pixel to its immediate right, P2. Further, it should be understoodthat replacement values may be retained or propagated for successivedefective pixel detection operations. For instance, referring to the setof horizontal pixels shown in FIG. 69, if P0 or P1 were previouslyidentified by the DPDC logic 738 as being defective pixels, theircorresponding replacement values may be used for the defective pixeldetection and replacement of the current pixel P.

To summarize the above-discussed defective pixel detection andcorrection techniques, a flow chart depicting such a process is providedin FIG. 70 and referred to by reference number 748. As shown, process748 begins at step 749, at which a current pixel (P) is received and aset of neighbor pixels is identified. In accordance with the embodimentdescribed above, the neighbor pixels may include two horizontal pixelsof the same color component from opposite sides of the current pixel(e.g., P0, P1, P2, and P3). Next, at step 750, horizontal pixel-to-pixelgradients are calculated with respect to each neighboring pixel withinthe raw frame 310, as described in Equation 8 above. Thereafter, at step751, a count C of the number of gradients that are less than or equal toa particular threshold dprTh is determined. As shown at decision logic752, if C is less than or equal to dprMaxC, then the process 748continues to step 753, and the current pixel is identified as beingdefective. The defective pixel is then corrected at step 754 using areplacement value. Additionally, referring back to decision logic 752,if C is greater than dprMaxC, then the process continues to step 755,and the current pixel is identified as not being defective, and itsvalue is not changed.

It should be noted that the defective pixel detection/correctiontechniques applied during the ISP front-end statistics processing may beless robust than defective pixel detection/correction that is performedin the ISP pipe logic 82. For instance, as will be discussed in furtherdetail below, defective pixel detection/correction performed in the ISPpipe logic 82 may, in addition to dynamic defect correction, furtherprovide for fixed defect correction, wherein the locations of defectivepixels are known a priori and loaded in one or more defect tables.Further, dynamic defect correction may in the ISP pipe logic 82 may alsoconsider pixel gradients in both horizontal and vertical directions, andmay also provide for the detection/correction of speckling, as will bediscussed below.

Returning to FIG. 68, the output of the DPDC logic 738 is then passed tothe black level compensation (BLC) logic 739. The BLC logic 739 mayprovide for digital gain, offset, and clipping independently for eachcolor component “c” (e.g., R, B, Gr, and Gb for Bayer) on the pixelsused for statistics collection. For instance, as expressed by thefollowing operation, the input value for the current pixel is firstoffset by a signed value, and then multiplied by a gain.

Y=(X+O[c])×G[c],  (11)

wherein X represents the input pixel value for a given color component c(e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for thecurrent color component c, and G[c] represents a gain value for thecolor component c. In one embodiment, the gain G[c] may be a 16-bitunsigned number with 2 integer bits and 14 fraction bits (e.g., 2.14 infloating point representation), and the gain G[c] may be applied withrounding. By way of example only, the gain G[c] may have a range ofbetween 0 to 4× (e.g., 4 times the input pixel value).

Next, as shown by Equation 12 below, the computed value Y, which issigned, may then be then clipped to a minimum and maximum range:

Y=(Y<min[c])?min[c]:(Y>max[c])?max[c]:Y)  (12)

The variables min[c] and max[c] may represent signed 16-bit “clippingvalues for the minimum and maximum output values, respectively. In oneembodiment, the BLC logic 739 may also be configured to maintain a countof the number of pixels that were clipped above and below maximum andminimum, respectively, per color component.

Subsequently, the output of the BLC logic 739 is forwarded to the lensshading correction (LSC) logic 740. The LSC logic 740 may be configuredto apply an appropriate gain on a per-pixel basis to compensate fordrop-offs in intensity, which are generally roughly proportional to thedistance from the optical center of the lens 88 of the imaging device30. As can be appreciated, such drop-offs may be the result of thegeometric optics of the lens. By way of example, a lens having idealoptical properties may be modeled as the fourth power of the cosine ofthe incident angle, cos⁴(θ), referred to as the cos⁴ law. However,because lens manufacturing is not perfect, various irregularities in thelens may cause the optical properties to deviate from the assumed cos⁴model. For instance, the thinner edged of the lens usually exhibits themost irregularities. Additionally, irregularities in lens shadingpatterns may also be the result of a microlens array within an imagesensor not being perfectly aligned with the color array filter. Further,the infrared (IR) filter in some lenses may cause the drop-off to beilluminant-dependent and, thus, lens shading gains may be adapteddepending upon the light source detected.

Referring to FIG. 71, a three-dimensional profile 756 depicting lightintensity versus pixel position for a typical lens is illustrated. Asshown, the light intensity near the center 757 of the lens graduallydrops off towards the corners or edges 758 of the lens. The lens shadingirregularities depicted in FIG. 71 may be better illustrated by FIG. 72,which shows a colored drawing of an image 759 that exhibits drop-offs inlight intensity towards the corners and edges. Particularly, it shouldbe noted that the light intensity at the approximate center of the imageappears to be brighter than the light intensity at the corners and/oredges of the image.

In accordance with embodiments of the present techniques, lens shadingcorrection gains may be specified as a two-dimensional grid of gains percolor channel (e.g., Gr, R, B, Gb for a Bayer filter). The gain gridpoints may be distributed at fixed horizontal and vertical intervalswithin the raw frame 310 (FIG. 23). As discussed above in FIG. 23, theraw frame 310 may include an active region 312 which defines an area onwhich processing is performed for a particular image processingoperation. With regard to the lens shading correction operation, anactive processing region, which may be referred to as the LSC region, isdefined within the raw frame region 310. As will be discussed below, theLSC region must be completely inside or at the gain grid boundaries,otherwise results may be undefined.

For instance, referring to FIG. 73, an LSC region 760 and a gain grid761 that may be defined within the raw frame 310 are shown. The LSCregion 760 may have a width 762 and a height 763, and may be defined byan x-offset 764 and a y-offset 765 with respect to the boundary of theraw frame 310. Grid offsets (e.g., grid x-offset 766 and grid y-offset767) from the base 768 of the grid gains 761 to the first pixel 769 inthe LSC region 760 is also provided. These offsets may be within thefirst grid interval for a given color component. The horizontalα-direction) and vertical (y-direction) grid point intervals 770 and771, respectively, may be specified independently for each colorchannel.

As discussed above, assuming the use of a Bayer color filter array, 4color channels of grid gains (R, B, Gr, and Gb) may be defined. In oneembodiment, a total of 4K (4096) grid points may be available, and foreach color channel, a base address for the start location of grid gainsmay be provided, such as by using a pointer. Further, the horizontal(770) and vertical (771) grid point intervals may be defined in terms ofpixels at the resolution of one color plane and, in certain embodiments,may be provide for grid point intervals separated by a power of 2, suchas by 8, 16, 32, 64, or 128, etc., in horizontal and verticaldirections. As can be appreciated, by utilizing a power of 2, efficientimplementation of gain interpolation using a shift (e.g., division) andadd operations may be achieved. Using these parameters, the same gainvalues can be used even as the image sensor cropping region is changing.For instance, only a few parameters need to be updated to align the gridpoints to the cropped region (e.g., updating the grid offsets 770 and771) instead of updating all grid gain values. By way of example only,this may be useful when cropping is used during digital zoomingoperations. Further, while the gain grid 761 shown in the embodiment ofFIG. 73 is depicted as having generally equally spaced grid points, itshould be understood that in other embodiments, the grid points may notnecessarily be equally spaced. For instance, in some embodiments, thegrid points may be distributed unevenly (e.g., logarithmically), suchthat the grid points are less concentrated in the center of the LSCregion 760, but more concentrated towards the corners of the LSC region760, typically where lens shading distortion is more noticeable.

In accordance with the presently disclosed lens shading correctiontechniques, when a current pixel location is located outside of the LSCregion 760, no gain is applied (e.g., the pixel is passed unchanged).When the current pixel location is at a gain grid location, the gainvalue at that particular grid point may be used. However, when a currentpixel location is between grid points, the gain may be interpolatedusing bi-linear interpolation. An example of interpolating the gain forthe pixel location “G” on FIG. 74 is provided below.

As shown in FIG. 74, the pixel G is between the grid points G0, G1, G2,and G3, which may correspond to the top-left, top-right, bottom-left,and bottom-right gains, respectively, relative to the current pixellocation G. The horizontal and vertical size of the grid interval isrepresented by X and Y, respectively. Additionally, ii and jj representthe horizontal and vertical pixel offsets, respectively, relative to theposition of the top left gain G0. Based upon these factors, the gaincorresponding to the position G may thus be interpolated as follows:

$\begin{matrix}{G = \frac{\begin{matrix}{\left( {G\; 0\left( {Y - {jj}} \right)\left( {X - {ii}} \right)} \right) + \left( {G\; 1\left( {Y - {jj}} \right)({ii})} \right) +} \\{\left( {G\; 2({jj})\left( {X - {ii}} \right)} \right) + \left( {G\; 3({ii})({jj})} \right)}\end{matrix}}{XY}} & \left( {13a} \right)\end{matrix}$

The terms in Equation 13a above may then be combined to obtain thefollowing expression:

$\begin{matrix}{G = \frac{\begin{matrix}{{G\; {0\left\lbrack {{XY} - {X({jj})} - {Y({ii})} + {({ii})({jj})}} \right\rbrack}} +} \\{{G\; {1\left\lbrack {{Y({ii})} - {({ii})({jj})}} \right\rbrack}} + {G\; {2\left\lbrack {{X({jj})} - {({ii})({jj})}} \right\rbrack}} + {G\; {3\left\lbrack {({ii})({jj})} \right\rbrack}}}\end{matrix}}{XY}} & \left( {13b} \right)\end{matrix}$

In one embodiment, the interpolation method may be performedincrementally, instead of using a multiplier at each pixel, thusreducing computational complexity. For instance, the term (ii)(jj) maybe realized using an adder that may be initialized to 0 at location (0,0) of the gain grid 761 and incremented by the current row number eachtime the current column number increases by a pixel. As discussed above,since the values of X and Y may be selected as powers of two, gaininterpolation may be accomplished using a simple shift operations. Thus,the multiplier is needed only at the grid point G0 (instead of at everypixel), and only addition operations are needed to determine theinterpolated gain for the remaining pixels.

In certain embodiments, the interpolation of gains between the gridpoints may use 14-bit precision, and the grid gains may be unsigned10-bit values with 2 integer bits and 8 fractional bits (e.g., 2.8floating point representation). Using this convention, the gain may havea range of between 0 and 4×, and the gain resolution between grid pointsmay be 1/256.

The lens shading correction techniques may be further illustrated by theprocess 772 shown in FIG. 75. As shown, process 772 begins at step 773,at which the position of a current pixel is determined relative to theboundaries of the LSC region 760 of FIG. 73. Next, decision logic 774determines whether the current pixel position is within the LSC region760. If the current pixel position is outside of the LSC region 760, theprocess 772 continues to step 775, and no gain is applied to the currentpixel (e.g., the pixel passes unchanged).

If the current pixel position is within the LSC region 760, the process772 continues to decision logic 776, at which it is further determinedwhether the current pixel position corresponds to a grid point withinthe gain grid 761. If the current pixel position corresponds to a gridpoint, then the gain value at that grid point is selected and applied tothe current pixel, as shown at step 777. If the current pixel positiondoes not correspond to a grid point, then the process 772 continues tostep 778, and a gain is interpolated based upon the bordering gridpoints (e.g., G0, G1, G2, and G3 of FIG. 74). For instance, theinterpolated gain may be computed in accordance with Equations 13a and13b, as discussed above. Thereafter, the process 772 ends at step 779,at which the interpolated gain from step 778 is applied to the currentpixel.

As will be appreciated, the process 772 may be repeated for each pixelof the image data. For instance, as shown in FIG. 76, athree-dimensional profile depicting the gains that may be applied toeach pixel position within a LSC region (e.g. 760) is illustrated. Asshown, the gain applied at the corners 780 of the image may be generallygreater than the gain applied to the center 781 of the image due to thegreater drop-off in light intensity at the corners, as shown in FIGS. 71and 72. Using the presently described lens shading correctiontechniques, the appearance of light intensity drop-offs in the image maybe reduced or substantially eliminated. For instance, FIG. 77 providesan example of how the colored drawing of the image 759 from FIG. 72 mayappear after lens shading correction is applied. As shown, compared tothe original image from FIG. 72, the overall light intensity isgenerally more uniform across the image. Particularly, the lightintensity at the approximate center of the image may be substantiallyequal to the light intensity values at the corners and/or edges of theimage. Additionally, as mentioned above, the interpolated gaincalculation (Equations 13a and 13b) may, in some embodiments, bereplaced with an additive “delta” between grid points by takingadvantage of the sequential column and row incrementing structure. Aswill be appreciated, this reduces computational complexity.

In further embodiments, in addition to using grid gains, a global gainper color component that is scaled as a function of the distance fromthe image center is used. The center of the image may be provided as aninput parameter, and may be estimated by analyzing the light intensityamplitude of each image pixel in the uniformly illuminated image. Theradial distance between the identified center pixel and the currentpixel, may then be used to obtain a linearly scaled radial gain, G_(r),as shown below:

G _(p) =G _(p) [c]×R,  (14)

wherein G_(p)[α] represents a global gain parameter for each colorcomponent c (e.g., R, B, Gr, and Gb components for a Bayer pattern), andwherein R represents the radial distance between the center pixel andthe current pixel.

With reference to FIG. 78, which shows the LSC region 760 discussedabove, the distance R may be calculated or estimated using severaltechniques. As shown, the pixel C corresponding to the image center mayhave the coordinates (x₀, y₀), and the current pixel G may have thecoordinates (x_(G), y_(G)). In one embodiment, the LSC logic 740 maycalculate the distance R using the following equation:

R=√{square root over ((x _(G) −x ₀)²+(y _(G) −y ₀)²)}{square root over((x _(G) −x ₀)²+(y _(G) −y ₀)²)}  (15)

In another embodiment, a simpler estimation formula, shown below, may beutilized to obtain an estimated value for R.

R=α×max(abs(x _(G) −x ₀),abs(y _(G) −y ₀))+β×min(abs(x _(G) −x ₀),abs(y_(G) −y ₀))  (16)

In Equation 16, the estimation coefficients α and β may be scaled to8-bit values. By way of example only, in one embodiment, a may be equalto approximately 123/128 and β may be equal to approximately 51/128 toprovide an estimated value for R. Using these coefficient values, thelargest error may be approximately 4%, with a median error ofapproximately 1.3%. Thus, even though the estimation technique may besomewhat less accurate than utilizing the calculation technique indetermining R (Equation 15), the margin of error is low enough that theestimated values or R are suitable for determining radial gaincomponents for the present lens shading correction techniques.

The radial gain G_(r) may then be multiplied by the interpolated gridgain value G (Equations 13a and 13b) for the current pixel to determinea total gain that may be applied to the current pixel. The output pixelY is obtained by multiplying the input pixel value X with the totalgain, as shown below:

Y=(G×G _(r) ×X)  (17)

Thus, in accordance with the present technique, lens shading correctionmay be performed using only the interpolated gain, both the interpolatedgain and the radial gain components. Alternatively, lens shadingcorrection may also be accomplished using only the radial gain inconjunction with a radial grid table that compensates for radialapproximation errors. For example, instead of a rectangular gain grid761, as shown in FIG. 73, a radial gain grid having a plurality of gridpoints defining gains in the radial and angular directions may beprovided. Thus, when determining the gain to apply to a pixel that doesnot align with one of the radial grid points within the LSC region 760,interpolation may be applied using the four grid points that enclose thepixel to determine an appropriate interpolated lens shading gain.

Referring to FIG. 79, the use of interpolated and radial gain componentsin lens shading correction is illustrated by the process 782. It shouldbe noted that the process 782 may include steps that are similar to theprocess 772, described above in FIG. 75. Accordingly, such steps havebeen numbered with like reference numerals. Beginning at step 773, thecurrent pixel is received and its location relative to the LSC region760 is determined. Next, decision logic 774 determines whether thecurrent pixel position is within the LSC region 760. If the currentpixel position is outside of the LSC region 760, the process 782continues to step 775, and no gain is applied to the current pixel(e.g., the pixel passes unchanged). If the current pixel position iswithin the LSC region 760, then the process 782 may continuesimultaneously to step 783 and decision logic 776. Referring first tostep 783, data identifying the center of the image is retrieved. Asdiscussed above, determining the center of the image may includeanalyzing light intensity amplitudes for the pixels under uniformillumination. This may occur during calibration, for instance. Thus, itshould be understood that step 783 does not necessarily encompassrepeatedly calculating the center of the image for processing eachpixel, but may refer to retrieving the data (e.g., coordinates) ofpreviously determined image center. Once the center of the image isidentified, the process 782 may continue to step 784, wherein thedistance between the image center and the current pixel location (R) isdetermined. As discussed above, the value of R may be calculated(Equation 15) or estimated (Equation 16). Then, at step 785, a radialgain component G_(r) may be computed using the distance R and globalgain parameter corresponding to the color component of the current pixel(Equation 14). The radial gain component G_(r) may be used to determinethe total gain, as will be discussed in step 787 below.

Referring back to decision logic 776, a determined whether the currentpixel position corresponds to a grid point within the gain grid 761. Ifthe current pixel position corresponds to a grid point, then the gainvalue at that grid point is determined, as shown at step 786. If thecurrent pixel position does not correspond to a grid point, then theprocess 782 continues to step 778, and an interpolated gain is computedbased upon the bordering grid points (e.g., G0, G1, G2, and G3 of FIG.74). For instance, the interpolated gain may be computed in accordancewith Equations 13a and 13b, as discussed above. Next, at step 787, atotal gain is determined based upon the radial gain determined at step785, as well as one of the grid gains (step 786) or the interpolatedgain (778). As can be appreciated, this may depend on which branchdecision logic 776 takes during the process 782. The total gain is thenapplied to the current pixel, as shown at step 788. Again, it should benoted that like the process 772, the process 782 may also be repeatedfor each pixel of the image data.

The use of the radial gain in conjunction with the grid gains may offervarious advantages. For instance, using a radial gain allows for the useof single common gain grid for all color components. This may greatlyreduce the total storage space required for storing separate gain gridsfor each color component. For instance, in a Bayer image sensor, the useof a single gain grid for each of the R, B, Gr, and Gb components mayreduce the gain grid data by approximately 75%. As will be appreciated,this reduction in grid gain data may decrease implementation costs, asgrid gain data tables may account for a significant portion of memory orchip area in image processing hardware. Further, depending upon thehardware implementation, the use of a single set of gain grid values mayoffer further advantages, such as reducing overall chip area (e.g., suchas when the gain grid values are stored in an on-chip memory) andreducing memory bandwidth requirements (e.g., such as when the gain gridvalues are stored in an off-chip external memory).

Having thoroughly described the functionalities of the lens shadingcorrection logic 740 shown in FIG. 68, the output of the LSC logic 740is subsequently forwarded to the inverse black level compensation (IBLC)logic 741. The IBLC logic 741 provides gain, offset and clipindependently for each color component (e.g., R, B, Gr, and Gb), andgenerally performs the inverse function to the BLC logic 739. Forinstance, as shown by the following operation, the value of the inputpixel is first multiplied by a gain and then offset by a signed value.

Y=(X×G[c])+O[c],  (18)

wherein X represents the input pixel value for a given color component c(e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for thecurrent color component c, and G[c] represents a gain value for thecolor component c. In one embodiment, the gain G[c] may have a range ofbetween approximately 0 to 4× (4 times the input pixel value X). Itshould be noted that these variables may be the same variables discussedabove in Equation 11. The computed value Y may be clipped to a minimumand maximum range using, for example, Equation 12. In one embodiment,the IBLC logic 741 may be configured to maintain a count of the numberof pixels that were clipped above and below maximum and minimum,respectively, per color component.

Thereafter, the output of the IBLC logic 741 is received by thestatistics collection block 742, which may provide for the collection ofvarious statistical data points about the image sensor(s) 90, such asthose relating to auto-exposure (AE), auto-white balance (AWB),auto-focus (AF), flicker detection, and so forth. With this in mind, adescription certain embodiments of the statistics collection block 742and various aspects related thereto is provided below with respect toFIGS. 80-97.

As will be appreciated, AWB, AE, and AF statistics may be used in theacquisition of images in digital still cameras as well as video cameras.For simplicity, AWB, AE, and AF statistics may be collectively referredto herein as “3A statistics.” In the embodiment of the ISP front-endlogic illustrated in FIG. 68, the architecture for the statisticscollection logic 742 (“3A statistics logic”) may be implemented inhardware, software, or a combination thereof. Further, control softwareor firmware may be utilized to analyze the statistics data collected bythe 3A statistics logic 742 and control various parameters of the lens(e.g., focal length), sensor (e.g., analog gains, integration times),and the ISP pipeline 82 (e.g., digital gains, color correction matrixcoefficients). In certain embodiments, the image processing circuitry 32may be configured to provide flexibility in statistics collection toenable control software or firmware to implement various AWB, AE, and AFalgorithms.

With regard to white balancing (AWB), the image sensor response at eachpixel may depend on the illumination source, since the light source isreflected from objects in the image scene. Thus, each pixel valuerecorded in the image scene is related to the color temperature of thelight source. For instance, FIG. 79 shows a graph 789 illustrating thecolor range of white areas under low color and high color temperaturesfor a YCbCr color space. As shown, the x-axis of the graph 789represents the blue-difference chroma (Cb) and the y-axis of the graph789 represents red-difference chroma (Cr) of the YCbCr color space. Thegraph 789 also shows a low color temperature axis 790 and a high colortemperature axis 791. The region 792 in which the axes 790 and 791 arepositioned, represents the color range of white areas under low and highcolor temperatures in the YCbCr color space. It should be understood,however, that the YCbCr color space is merely one example of a colorspace that may be used in conjunction with auto white balance processingin the present embodiment. Other embodiments may utilize any suitablecolor space. For instance, in certain embodiments, other suitable colorspaces may include a Lab (CIELab) color space (e.g., based on CIE 1976),a red/blue normalized color space (e.g., an R/(R+2G+B) and B/(R+2G+B)color space; a R/G and B/G color space; a Cb/Y and Cr/Y color space,etc.). Accordingly, for the purposes of this disclosure, the axes of thecolor space used by the 3A statistics logic 742 may be referred to as C1and C2 (as is the case in FIG. 80).

When a white object is illuminated under a low color temperature, it mayappear reddish in the captured image. Conversely, a white object that isilluminated under a high color temperature may appear bluish in thecaptured image. The goal of white balancing is, therefore, to adjust RGBvalues such that the image appears to the human eye as if it were takenunder canonical light. Thus, in the context of imaging statisticsrelating to white balance, color information about white objects arecollected to determine the color temperature of the light source. Ingeneral, white balance algorithms may include two main steps. First, thecolor temperature of the light source is estimated. Second, theestimated color temperature is used to adjust color gain values and/ordetermine/adjust coefficients of a color correction matrix. Such gainsmay be a combination of analog and digital image sensor gains, as wellas ISP digital gains.

For instance, in some embodiments, the imaging device 30 may becalibrated using multiple different reference illuminants. Accordingly,the white point of the current scene may be determined by selecting thecolor correction coefficients corresponding to a reference illuminantthat most closely matches the illuminant of the current scene. By way ofexample only, one embodiment may calibrate the imaging device 30 usingfive reference illuminants, a low color temperature illuminant, amiddle-low color temperature illuminant, a middle color temperatureilluminant, a middle-high color temperature illuminant, and a high colortemperature illuminant. As shown in FIG. 81, one embodiment may definewhite balance gains using the following color correction profiles:Horizon (H) (simulating a color temperature of approximately 2300degrees), Incandescent (A or IncA) (simulating a color temperature ofapproximately 2856 degrees), D50 (simulating a color temperature ofapproximately 5000 degrees), D65 (simulating a color temperature ofapproximately 6500 degrees), and D75 (simulating a color temperature ofapproximately 7500 degrees).

Depending on the illuminant of the current scene, white balance gainsmay be determined using the gains corresponding to the referenceilluminant that most closely matches the current illuminant. Forinstance, if the statistics logic 742 (described in more detail in FIG.82 below) determines that the current illuminant approximately matchesthe reference middle color temperature illuminant, D50, then whitebalance gains of approximately 1.37 and 1.23 may be applied to the redand blue color channels, respectively, while approximately no gain (1.0)is applied to the green channels (G0 and G1 for Bayer data). In someembodiments, if the current illuminant color temperature is in betweentwo reference illuminants, white balance gains may be determined viainterpolating the white balance gains between the two referenceilluminants. Further, while the present example shows an imaging devicebeing calibrated using H, A, D50, D65, and D75 illuminants, it should beunderstood that any suitable type of illuminant may be used for cameracalibration, such as TL84 or CWF (fluorescent reference illuminants),and so forth.

As will be discussed further below, several statistics may be providedfor AWB including a two-dimensional (2D) color histogram, and RGB or YCCsums to provide multiple programmable color ranges. For instance, in oneembodiment, the statistics logic 742 may provide a set of multiple pixelfilters, of which a subset of the multiple pixel filters may be selectedfor AWB processing. In one embodiment, eight sets of filters, each withdifferent configurable parameters, may be provided, and three sets ofcolor range filters may be selected from the set for gathering tilestatistics, as well as for gathering statistics for each floatingwindow. By way of example, a first selected filter may be configured tocover the current color temperature to obtain accurate color estimation,a second selected filter may be configured to cover the low colortemperature areas, and a third selected filter may be configured tocover the high color temperature areas. This particular configurationmay enable the AWB algorithm to adjust the current color temperaturearea as the light source is changing. Further, the 2D color histogrammay be utilized to determine the global and local illuminants and todetermine various pixel filter thresholds for accumulating RGB values.Again, it should be understood that the selection of three pixel filtersis meant to illustrate just one embodiment. In other embodiments, feweror more pixel filters may be selected for AWB statistics.

Further, in addition to selecting three pixel filters, one additionalpixel filter may also be used for auto-exposure (AE), which generallyrefers to a process of adjusting pixel integration time and gains tocontrol the luminance of the captured image. For instance, auto-exposuremay control the amount of light from the scene that is captured by theimage sensor(s) by setting the integration time. In certain embodiments,tiles and floating windows of luminance statistics may be collected viathe 3A statistics logic 742 and processed to determine integration andgain control parameters.

Further, auto-focus may refer to determining the optimal focal length ofthe lens in order to substantially optimize the focus of the image. Incertain embodiments, floating windows of high frequency statistics maybe collected and the focal length of the lens may be adjusted to bringan image into focus. As discussed further below, in one embodiment,auto-focus adjustments may utilize coarse and fine adjustments basedupon one or more metrics, referred to as auto-focus scores (AF scores)to bring an image into focus. Further, in some embodiments, AFstatistics/scores may be determined for different colors, and therelativity between the AF statistics/scores for each color channel maybe used to determine the direction of focus.

Thus, these various types of statistics, among others, may be determinedand collected via the statistics collection block 742. As shown, theoutput STATS0 of the statistics collection block 742 of the Sensor0statistics processing unit 142 may be sent to the memory 108 and routedto the control logic 84 or, alternatively, may be sent directly to thecontrol logic 84. Further, it should be understood that the Sensor1statistics processing unit 144 may also include a similarly configured3A statistics collection block that provides statistics STATS1, as shownin FIG. 10.

As discussed above, the control logic 84, which may be a dedicatedprocessor in the ISP subsystem 32 of the device 10, may process thecollected statistical data to determine one or more control parametersfor controlling the imaging device 30 and/or the image processingcircuitry 32. For instance, such the control parameters may includeparameters for operating the lens of the image sensor 90 (e.g., focallength adjustment parameters), image sensor parameters (e.g., analogand/or digital gains, integration time), as well as ISP pipe processingparameters (e.g., digital gain values, color correction matrix (CCM)coefficients). Additionally, as mentioned above, in certain embodiments,statistical processing may occur at a precision of 8-bits and, thus, rawpixel data having a higher bit-depth may be down-scaled to an 8-bitformat for statistics purposes. As discussed above, down-scaling to8-bits (or any other lower-bit resolution) may reduce hardware size(e.g., area) and also reduce processing complexity, as well as allow forthe statistics data to be more robust to noise (e.g., using spatialaveraging of the image data).

With the foregoing in mind, FIG. 82 is a block diagram depicting logicfor implementing one embodiment of the 3A statistics logic 742. Asshown, the 3A statistics logic 742 may receive a signal 793 representingBayer RGB data which, as shown in FIG. 68, may correspond to the outputof the inverse BLC logic 741. The 3A statistics logic 742 may processthe Bayer RGB data 793 to obtain various statistics 794, which mayrepresent the output STATS0 of the 3A statistics logic 742, as shown inFIG. 68, or alternatively the output STATS1 of a statistics logicassociated with the Sensor1 statistics processing unit 144.

In the illustrated embodiment, for the statistics to be more robust tonoise, the incoming Bayer RGB pixels 793 are first averaged by the logic795. For instance, the averaging may be performed in a window size of4×4 sensor pixels consisting of four 2×2 Bayer quads (e.g., a 2×2 blockof pixels representing the Bayer pattern), and the averaged red (R),green (G), and blue (B) values in the 4×4 window may be computed andconverted to 8-bits, as mentioned above. This process is illustrates inmore detail with respect to FIG. 83, which shows a 4×4 window 796 ofpixels formed as four 2×2 Bayer quads 797. Using this arrangement, eachcolor channel includes a 2×2 block of corresponding pixels within thewindow 796, and same-colored pixels may be summed and averaged toproduce an average color value for each color channel within the window796. For instance, red pixels 799 may be averaged to obtain an averagered value (R_(AV)) 803, and the blue pixels 800 may be averaged toobtain an average blue value (B_(AV)) 804 within the sample 796. Withregard to averaging of the green pixels, several techniques may beutilized since the Bayer pattern has twice as many green samples as redor blue samples. In one embodiment, the average green value (G_(Av)) 802may be obtained by averaging just the Gr pixels 798, just the Gb pixels801, or all of the Gr and Gb pixels 798 and 801 together. In anotherembodiment, the Gr and Gb pixels 798 and 801 in each Bayer quad 797 maybe averaged, and the average of the green values for each Bayer quad 797may be further averaged together to obtain G_(AV) 802. As will beappreciated, the averaging of the pixel values across pixel blocks mayprovide for the reduction of noise. Further, it should be understoodthat the use of a 4×4 block as a window sample is merely intended toprovide one example. Indeed, in other embodiments, any suitable blocksize may be utilized (e.g., 8×8, 16×16, 32×32, etc.).

Thereafter, the down-scaled Bayer RGB values 806 are input to the colorspace conversion logic units 807 and 808. Because some of the 3Astatistics data may rely upon pixel pixels after applying color spaceconversion, the color space conversion (CSC) logic 807 and CSC logic 808may be configured to convert the down-sampled Bayer RGB values 806 intoone or more other color spaces. In one embodiment, the CSC logic 807 mayprovide for a non-linear space conversion and the CSC logic 808 mayprovide for a linear space conversion. Thus, the CSC logic units 807 and808 may convert the raw image data from sensor Bayer RGB to anothercolor space (e.g., sRGB_(linear), sRGB, YCbCr, etc.) that may be moreideal or suitable for performing white point estimation for whitebalance.

In the present embodiment, the non-linear CSC logic 807 may beconfigured to perform a 3×3 matrix multiply, followed by a non-linearmapping implemented as a lookup table, and further followed by another3×3 matrix multiply with an added offset. This allows for the 3Astatistics color space conversion to replicate the color processing ofthe RGB processing in the ISP pipeline 82 (e.g., applying white balancegain, applying a color correction matrix, applying RGB gammaadjustments, and performing color space conversion) for a given colortemperature. It may also provide for the conversion of the Bayer RGBvalues to a more color consistent color space such as CIELab, or any ofthe other color spaces discussed above (e.g., YCbCr, a red/bluenormalized color space, etc.). Under some conditions, a Lab color spacemay be more suitable for white balance operations because thechromaticity is more linear with respect to brightness.

As shown in FIG. 82, the output pixels from the Bayer RGB down-scaledsignal 806 are processed with a first 3×3 color correction matrix(3A_CCM), referred to herein by reference number 808. In the presentembodiment, the 3A_CCM 809 may be configured to convert from a cameraRGB color space (camRGB), to a linear sRGB calibrated space(sRGB_(linear)). A programmable color space conversion that may be usedin one embodiment is provided below by Equations 19-21:

sR_(linear)=max(0,min(255,(3A_CCM_(—)00*R+3A_CCM_(—)01*G+3A_CCM_(—)02*B)));  (19)

sG_(linear)=max(0,min(255,(3A_CCM_(—)10*R+3A_CCM_(—)11*G+3A_CCM_(—)12*B)));  (20)

sB_(linear)=max(0,min(255,(3A_CCM_(—)20*R+3A_CCM_(—)21*G+3A_CCM_(—)22*B)));  (21)

wherein 3A_CCM_(—)00-3A_CCM_22 represent signed coefficients of thematrix 808. Thus, each of the sR_(linear), sG_(linear), and sB_(linear),components of the sRGB_(linear) color space may be determined firstdetermining the sum of the red, blue, and green down-sampled Bayer RGBvalues with corresponding 3A_CCM coefficients applied, and then clippingthis value to either 0 or 255 (the minimum and maximum pixel values for8-bit pixel data) if the value exceeds 255 or is less than 0. Theresulting sRGB_(linear) values are represented in FIG. 82 by referencenumber 810 as the output of the 3A_CCM 809. Additionally, the 3Astatistics logic 742 may maintain a count of the number of clippedpixels for each of the sR_(linear), sG_(linear), and sB_(linear)components, as expressed below:

3A_CCM_(—) R_clipcount_low:number of sR _(linear) pixels<0 clipped

3A_CCM_(—) R_clipcount_high:number of sR _(linear) pixels>255 clipped

3A_CCM_(—) G_clipcount_low:number of sG _(linear) pixels<0 clipped

3A_CCM_(—) G_clipcount_high:number of sG _(linear) pixels>255 clipped

3A_CCM_(—) B_clipcount_low:number of sB _(linear) pixels<0 clipped

3A_CCM_B_clipcount_high:number of sB _(linear) pixels>255 clipped

Next, the sRGB_(linear) pixels 810 may be processed using a non-linearlookup table 811 to produce sRGB pixels 812. The lookup table 811 maycontain entries of 8-bit values, with each table entry valuerepresenting an output levels. In one embodiment, the look-up table 811may include 65 evenly distributed input entries, wherein a table indexrepresents input values in steps of 4. When the input value fallsbetween intervals, the output values are linearly interpolated.

As will be appreciated, the sRGB color space may represent the colorspace of the final image produced by the imaging device 30 (FIG. 7) fora given white point, as white balance statistics collection is performedin the color space of the final image produced by the image device. Inone embodiment, a white point may be determined by matching thecharacteristics of the image scene to one or more reference illuminantsbased, for example, upon red-to-green and/or blue-to-green ratios. Forinstance, one reference illuminant may be D65, a CIE standard illuminantfor simulating daylight conditions. In addition to D65, calibration ofthe imaging device 30 may also be performed for other differentreference illuminants, and the white balance determination process mayinclude determining a current illuminant so that processing (e.g., colorbalancing) may be adjusted for the current illuminant based oncorresponding calibration points. By way of example, in one embodiment,the imaging device 30 and 3A statistics logic 742 may be calibratedusing, in addition to D65, a cool white fluorescent (CWF) referenceilluminant, the TL84 reference illuminant (another fluorescent source),and the IncA (or A) reference illuminant, which simulates incandescentlighting. Additionally, as discussed above, various other illuminantscorresponding to different color temperatures (e.g., H, IncA, D50, D65,and D75, etc.) may also be used in camera calibration for white balanceprocessing. Thus, a white point may be determined by analyzing an imagescene and determining which reference illuminant most closely matchesthe current illuminant source.

Referring still to the non-linear CSC logic 807, the sRGB pixel output812 of the look-up table 811 may be further processed with a second 3×3color correction matrix 813, referred to herein as 3A_CSC. In thedepicted embodiment, the 3A_CSC matrix 813 is shown as being configuredto convert from the sRGB color space to the YCbCr color space, though itmay be configured to convert the sRGB values into other color spaces aswell. By way of example, the following programmable color spaceconversion (Equations 22-27) may be used:

Y=3A_CSC_(—)00*sR+3A_CSC_(—)01*sG+3A_CSC_(—)02*sB+3A_OffsetY;  (22)

Y=max(3A_CSC_MIN_(—) Y,min(3A_CSC_MAX_(—) Y,Y));  (23)

C1=3A_CSC_(—)10*sR+3A_CSC_(—)11*sG+3A_CSC_(—)12*sB+3A_OffsetC1;  (24)

C1=max(3A_CSC_MIN_(—) C1,min(3A_CSC_MAX_(—) C1,C1));  (25)

C2=3A_CSC_(—)20*sR+3A_CSC_(—)21*sG+3A_CSC_(—)22*sB+3A_OffsetC2;  (26)

C2=max(3A_CSC_MIN_(—) C2,min(3A_CSC_MAX_(—) C2,C2));  (27)

wherein 3A_CSC_(—)00-3A_CSC_(—)22 represent signed coefficients for thematrix 813, 3A_OffsetY, 3A_OffsetC1, and 3A_OffsetC2 represent signedoffsets, and C1 and C2 represent different colors, here blue-differencechroma (Cb) and red-difference chroma (Cr), respectively. It should beunderstood, however, that C1 and C2 may represent any suitabledifference chroma colors, and need not necessarily be Cb and Cr colors.

As shown in Equations 22-27, in determining each component of YCbCr,appropriate coefficients from the matrix 813 are applied to the sRGBvalues 812 and the result is summed with a corresponding offset (e.g.,Equations 22, 24, and 26). Essentially, this step is a 3×1 matrixmultiplication step. This result from the matrix multiplication is thenclipped between a maximum and minimum value (e.g., Equations 23, 25, and27). The associated minimum and maximum clipping values may beprogrammable and may depend, for instance, on particular imaging orvideo standards (e.g., BT.601 or BT.709) being utilized.

The 3A statistics logic 742 may also maintain a count of the number ofclipped pixels for each of the Y, C1, and C2 components, as expressedbelow:

3A_CSC_(—) Y_clipcount_low:number of Y pixels<3A_CSC_MIN_(—) Y clipped

3A_CSC_(—) Y_clipcount_high:number of Y pixels>3A_CSC_MAX_(—) Y clipped

3A_CSC_(—) C1_clipcount_high:number of C1 pixels>3A_CSC_MAX_(—) C1clipped

3A_CSC_(—) C2_clipcount_low:number of C2 pixels<3A_CSC_MIN_(—) C2clipped

3A_CSC_(—) C2_clipcount_high:number of C2 pixels>3A_CSC_MAX_(—) C2clipped

The output pixels from the Bayer RGB down-sample signal 806 may also beprovided to the linear color space conversion logic 808, which may beconfigured to implement a camera color space conversion. For instance,the output pixels 806 from the Bayer RGB down-sample logic 795 may beprocessed via another 3×3 color conversion matrix (3A_CSC2) 815 of theCSC logic 808 to convert from sensor RGB (camRGB) to a linearwhite-balanced color space (camYC1C2), wherein C1 and C2 may correspondto Cb and Cr, respectively. In one embodiment, the chroma pixels may bescaled by luma, which may be beneficial in implementing a color filterthat has improved color consistency and is robust to color shifts due toluma changes. An example of how the camera color space conversion may beperformed using the 3×3 matrix 815 is provided below in Equations 28-31:

camY=3A_CSC2_(—)00*R+3A_CSC2_(—)01*G+3A_CSC2_(—)02*B+3A_Offset2Y;  (28)

camY=max(3A_CSC2_MIN_(—) Y,min(3A_CSC2_MAX_(—) Y,camY));  (29)

camC1=(3A_CSC2_(—)10*R+3A_CSC2_(—)11*G+3A_CSC2_(—)12*B);  (30)

camC2=(3A_CSC2_(—)20*R+3A_CSC2_(—)21*G+3A_CSC2_(—)22*B);  (31)

wherein 3A_CSC2_(—)00-3A_CSC2_(—)22 represent signed coefficients forthe matrix 815, 3A_Offset2Y represents a signed offset for camY, andcamC1 and camC2 represent different colors, here blue-difference chroma(Cb) and red-difference chroma (Cr), respectively. As shown in Equation28, to determine camY, corresponding coefficients from the matrix 815are applied to the bayer RGB values 806, and the result is summed with3A_Offset2Y. This result is then clipped between a maximum and minimumvalue, as shown in Equation 29. As discussed above, the clipping limitsmay be programmable.

At this point, the camC1 and camC2 pixels of the output 816 are signed.As discussed above, in some embodiments, chroma pixels may be scaled.For example, one technique for implementing chroma scaling is shownbelow:

camC1=camC1*ChromaScale*255/(camY?camY:1);  (32)

camC2=camC2*ChromaScale*255/(camY?camY:1);  (33)

wherein ChromaScale represents a floating point scaling factor between 0and 8. In Equations 32 and 33, the expression (camY? camY:1) is meant toprevent a divide-by-zero condition. That is, if camY is equal to zero,the value of camY is set to 1. Further, in one embodiment, ChromaScalemay be set to one of two possible values depending on the sign of camC1.For instance, as shown below in Equation 34, ChomaScale may be set to afirst value (ChromaScale0) if camC1 is negative, or else may be set to asecond value (ChromaScale1):

ChromaScale=ChromaScale0 if(camC1<0)

ChromaScale1 otherwise  (34)

Thereafter, chroma offsets are added, and the camC1 and camC2 chromapixels are clipped, as shown below in Equations 35 and 36, to generatecorresponding unsigned pixel values:

camC1=max(3A_CSC2_MIN_(—) C1,min(3A_CSC2_MAX_(—)C1,(camC1+3A_Offset2C1)))  (35)

camC2=max(3A_CSC2_MIN_(—) C2,min(3A_CSC2_MAX_(—)C2,(camC2+3A_Offset2C2)))  (36)

wherein 3A_CSC2_(—)00-3A_CSC2_(—)22 are signed coefficients of thematrix 815, and 3A_Offset2C1 and 3A_Offset2C2 are signed offsets.Further, the number of pixels that are clipped for camY, camC1, andcamC2 are counted, as shown below:

3A_CSC2_(—) Y_clipcount_low:number of camY pixels<3A_CSC2_MIN_(—) Yclipped

3A_CSC2_(—) Y_clipcount_high:number of camY pixels>3A_CSC2_MAX_(—) Yclipped

3A_CSC2_(—) C1_clipcount_low:number of camC1 pixels<3A_CSC2_MIN_(—) C1clipped

3A_CSC2_(—) C1_clipcount_high:number of camC1 pixels>3A_CSC2_MAX_(—) C1clipped

3A_CSC2_(—) C2_clipcount_low:number of camC2 pixels<3A_CSC2_MIN_(—) C2clipped

3A_CSC2_(—) C2_clipcount_high:number of camC2 pixels>3A_CSC2_MAX_(—) C2clipped

Thus, the non-linear and linear color space conversion logic 807 and 808may, in the present embodiment, provide pixel data in various colorspaces: sRGB_(linear) (signal 810), sRGB (signal 812), YCbYr (signal814), and camYCbCr (signal 816). It should be understood that thecoefficients for each conversion matrix 809 (3A_CCM), 813 (3A_CSC), and815 (3A_CSC2), as well as the values in the look-up table 811, may beindependently set and programmed.

Referring still to FIG. 82, the chroma output pixels from either thenon-linear color space conversion (YCbCr 814) or the camera color spaceconversion (camYCbCr 816) may be used to generate a two-dimensional (2D)color histogram 817. As shown, selection logic 818 and 819, which may beimplemented as multiplexers or by any other suitable logic, may beconfigured to select between luma and chroma pixels from either thenon-linear or camera color space conversion. The selection logic 818 and819 may operate in response to respective control signals which, in oneembodiment, may be supplied by the main control logic 84 of the imageprocessing circuitry 32 (FIG. 7) and may be set via software.

For the present example, it may be assumed that the selection logic 818and 819 select the YC1C2 color space conversion (814), where the firstcomponent is Luma, and where C1, C2 are the first and second colors(e.g., Cb, Cr). A 2D histogram 817 in the C1-C2 color space is generatedfor one window. For instance, the window may be specified with a columnstart and width, and a row start and height. In one embodiment, thewindow position and size may be set as a multiple of 4 pixels, and 32×32bins may be used for a total of 1024 bins. The bin boundaries may be atfixed interval and, in order to allow for zooming and panning of thehistogram collection in specific areas of the color space, a pixelscaling and offset may defined.

The upper 5 bits (representing a total of 32 values) of C1 and C2 afteroffset and scaling may used to determine the bin. The bin indices for C1and C2, referred to herein by C1 index and C2 index, may be determinedas follows:

C1_index=((C1−C1_offset)>>(3−C1_scale)  (37)

C2_index=((C2−C2_offset)>>(3−C2_scale)  (38)

Once the indices are determined, the color histogram bins areincremented by a Count value (which may have a value of between 0 and 3in one embodiment) if the bin indices are in the range [0, 31], as shownbelow in Equation 39. Effectively, this allows for weighting the colorcounts based on luma values (e.g., brighter pixels are weighted moreheavily, instead of weighting everything equally (e.g., by 1)).

if(C1_index>=0&&C1_index<=31&&C2_index>=0&&C2_index<=31)

StatsCbCrHist[C2_index&31][C1_index&31]+=Count;  (39)

where Count is determined based on the selected luma value, Y in thisexample. As will be appreciated, the steps represented by Equations 37,38, and 39 may be implemented by a bin update logic block 821. Further,in one embodiment, multiple luma thresholds may be set to define lumaintervals. By way of example, four luma thresholds (Ythd0-Ythd3) maydefine five luma intervals, with Count values Count0-4 being defined foreach interval. For instance, Count0-Count4 may be selected (e.g., bypixel condition logic 820) based on luma thresholds as follows:

if(Y<=Ythd0)

Count=Count0

else if(Y<=Ythd1)

Count=Count1

else if(Y<=Ythd2)

Count=Count2

else if(Y<=Ythd3)

Count=Count3

else

Count=Count4  (40)

With the foregoing in mind, FIG. 84 illustrates the color histogram withscaling and offsets set to zero for both C1 and C2. The divisions withinthe CbCr space represent each of the 32×32 bins (1024 total bins). FIG.85 provides an example of zooming and panning within the 2D colorhistogram for additional precision, wherein the rectangular area 822where the small rectangle specifies the location of the 32×32 bins.

At the start of a frame of image data, bin values are initialized tozero. For each pixel going into the 2D color histogram 817, the bincorresponding to the matching C1C2 value is incremented by a determinedCount value (Count0-Count4) which, as discussed above, may be based onthe luma value. For each bin within the 2D histogram 817, the totalpixel count is reported as part of the collected statistics data (e.g.,STATS0). In one embodiment, the total pixel count for each bin may havea resolution of 22-bits, whereby an allocation of internal memory equalto 1024×22 bits is provided.

Referring back to FIG. 82, the Bayer RGB pixels (signal 806),sRGB_(linear) pixels (signal 810), sRGB pixels (signal 812), and YC1C2(e.g., YCbCr) pixels (signal 814) are provided to a set of pixel filters824 a-c, where by RGB, sRGB_(linear), sRGB, YC1C2, or camYC1C2 sums maybe accumulated conditionally upon either camYC1C2 or YC pixelconditions, as defined by each pixel filter 824. That is, Y, C1 and C2values from either output of the non-linear color space conversion(YC1C2) or the output of the camera color space conversion (camYC1C2)are used to conditionally select RGB, sRGB_(linear), sRGB or YC1C2values to accumulate. While the present embodiment depicts the 3Astatistics logic 742 as having 8 pixel filters (PF0-PF7) provided, itshould be understood that any number of pixel filters may be provided.

FIG. 86 shows a functional logic diagram depicting an embodiment of thepixel filters, specifically PF0 (824 a) and PF1 (824 b) from FIG. 82. Asshown, each pixel filter 824 includes a selection logic, which receivesthe Bayer RGB pixels, the sRGB_(linear) pixels, the sRGB pixels, and oneof either the YC1C2 or camYC1C2 pixels, as selected by another selectionlogic 826. By way of example, the selection logic 825 and 826 may beimplemented using multiplexers or any other suitable logic. Theselection logic 826 may select either YC or camYC1C2. The selection maybe made in response to a control signal which may be supplied by themain control logic 84 of the image processing circuitry 32 (FIG. 7)and/or set by software. Next, the pixel filter 824 may use logic 827 toevaluate the YC1C2 pixels (e.g., either non-linear or camera) selectedby the selection logic 826 against a pixel condition. Each pixel filter824 may use the selection circuit 825 to select one of either the BayerRGB pixels, sRGB_(linear) pixels, sRGB pixels, and YC1C2 or camYC1C2pixel depending on the output from the selection circuit 826.

Using the results of the evaluation, the pixels selected by theselection logic 825 may be accumulated (828). In one embodiment, thepixel condition may be defined using thresholds C1_min, C1_max, C2_min,C2_max, as shown in graph 789 of FIG. 80. A pixel is included in thestatistics if it satisfies the following conditions:

C1_min<=C1<=C1_max  1.

C2_min<=C2<=C2_max  2.

abs((C2_delta*C1)−(C1_delta*C2)+Offset)<distance_max  3.

Y _(min) <=Y<=Y _(max)  4.

Referring to graph 829 of FIG. 87, in one embodiment, the point 830represents the values (C2, C1) corresponding to the current YC1C2 pixeldata, as selected by the logic 826. C1 delta may be determined as thedifference between C1_(—)1 and C1_(—)0, and C2_delta may be determinedas the difference between C2_(—)1 and C2_(—)0. As shown in FIG. 87, thepoints (C1_(—)0, C2_(—)0) and (C1_(—)1, C2_(—)1) may define the minimumand maximum boundaries for C1 and C2. The Offset may be determined bymultiplying C1 delta by the value 832 (C2 intercept) at where the line831 intercepts the axis C2. Thus, assuming that Y, C1, and C2 satisfythe minimum and maximum boundary conditions, the selected pixels (BayerRGB, sRGB_(linear), sRGB, and YC1C2/camYC1C2) is included in theaccumulation sum if its distance 833 from the line 831 is less thandistance_max 834, which may be distance 833 in pixels from the linemultiplied by a normalization factor:

distance_max=distance*sqrt(C1_deltâ2+C2_deltâ2)

In the present embodiment, distance, C1_delta and C2_delta may have arange of −255 to 255. Thus, distance_max 834 may be represented by 17bits. The points (C1_(—)0, C2_(—)0) and (C1_(—)1, C2_(—)1), as well asparameters for determining distance_max (e.g., normalization factor(s)),may be provided as part of the pixel condition logic 827 in each pixelfilter 824. As will be appreciated, the pixel conditions 827 may beconfigurable/programmable.

While the example shown in FIG. 87 depicts a pixel condition based ontwo sets of points (C1_(—)0, C2_(—)0) and (C1_(—)1, C2_(—)1), inadditional embodiments, certain pixel filters may define more complexshapes and regions upon which pixel conditions are determined. Forinstance, FIG. 88 shows an embodiment where a pixel filter 824 maydefine a five-sided polygon 835 using points (C1_(—)0, C2_(—)0),(C1_(—)1, C2_(—)1), (C1_(—)2, C2_(—)2) and (C1_(—)3, C2_(—)3), and(C1_(—)4, C2_(—)4). Each side 836 a-836 e may define a line condition.However, unlike the case shown in FIG. 87 (e.g., the pixel may be oneither side of line 831 as long as distance_max is satisfied), thecondition may be that the pixel (C1, C2) must be located on the side ofthe line 836 a-836 e such that it is enclosed by the polygon 835. Thus,the pixel (C1, C2) is counted when the intersection of multiple lineconditions is met. For instance, in FIG. 88, such an intersection occurswith respect to pixel 837 a. However, pixel 837 b fails to satisfy theline condition for line 836 d and, therefore, would not be counted inthe statistics when processed by a pixel filter configured in thismanner.

In a further embodiment, shown in FIG. 89, a pixel condition may bedetermined based on overlapping shapes. For instance, FIG. 89 shows howa pixel filter 824 may have pixel conditions defined using twooverlapping shapes, here rectangles 838 a and 838 b defined by points(C1_(—)0, C2_(—)0), (C1_(—)1, C2_(—)1), (C1_(—)2, C2_(—)2) and (C1_(—)3,C2_(—)3) and points (C1_(—)4, C2_(—)4), (C1_(—)5, C2_(—)5), (C1_(—)6,C2_(—)6) and (C1_(—7), C2_(—)7), respectively. In this example, a pixel(C1, C2) may satisfy line conditions defined by such a pixel filter bybeing enclosed within the region collectively bounded by the shapes 838a and 838 b (e.g., by satisfying the line conditions of each linedefining both shapes). For instance, in FIG. 89, these conditions aresatisfied with respect to pixel 839 a. However, pixel 839 b fails tosatisfy these conditions (specifically with respect to line 840 a ofrectangle 838 a and line 840 b of rectangle 838 b) and, therefore, wouldnot be counted in the statistics when processed by a pixel filterconfigured in this manner.

For each pixel filter 824, qualifying pixels are identified based on thepixel conditions defined by logic 827 and, for qualifying pixel values,the following statistics may be collected by the 3A statistics engine742: 32-bit sums: (R_(sum), G_(sum), B_(sum)) or (sR_(linear) _(—)_(sum), sG_(linear) _(—) _(sum), sB_(linear) _(—) _(sum)), or (sR_(sum),sG_(sum), sB_(sum)) or (Y_(sum, C)1_(sum), C2_(sum)) and a 24-bit pixelcount, Count, which may represent the sum of the number of pixels thatwere included in the statistic. In one embodiment, software may use thesum to generate an average in within a tile or window.

When the camYC1C2 pixels are selected by logic 825 of a pixel filter824, color thresholds may be performed on scaled chroma values. Forinstance, since chroma intensity at the white points increases with lumavalue, the use of chroma scaled with the luma value in the pixel filter824 may, in some instances, provide results with improved consistency.For example, minimum and maximum luma conditions may allow the filter toignore dark and/or bright areas. If the pixel satisfies the YC1C2 pixelcondition, the RGB, sRGB_(linear), sRGB or YC1C2 values are accumulated.The selection of the pixel values by the selection logic 825 may dependon the type of information needed. For instance, for white balance,typically RGB or sRGB_(linear) pixels are selected. For detectingspecific conditions, such as sky, grass, skin tones, etc., a YCC or sRGBpixel set may be more suitable.

In the present embodiment, eight sets of pixel conditions may bedefined, one associated with each of the pixel filters PF0-PF7 824. Somepixel conditions may be defined to carve an area in the C1-C2 colorspace (FIG. 80) where the white point is likely to be. This may bedetermined or estimated based on the current illuminant. Then,accumulated RGB sums may be used to determine the current white pointbased on the R/G and/or B/G ratios for white balance adjustments.Further, some pixel conditions may be defined or adapted to performscene analysis and classifications. For example, some pixel filters 824and windows/tiles may be utilized to detect for conditions, such as bluesky in a top portion of an image frame, or green grass in a bottomportion of an image frame. This information can also be used to adjustwhite balance. Additionally, some pixel conditions may be defined oradapted to detect skin tones. For such filters, tiles may be used todetect areas of the image frame that have skin tone. By identifyingthese areas, the quality of skin tone may be improved by, for example,reducing the amount of noise filter in skin tone areas and/or decreasingthe quantization in the video compression in those areas to improvequality.

The 3A statistics logic 742 may also provide for the collection of lumadata. For instance, the luma value, camY, from the camera color spaceconversion (camYC1C2) may be used for accumulating luma sum statistics.In one embodiment, the following luma information is may be collected bythe 3A statistics logic 742:

Y _(sum):sum of camY

cond(Y _(sum)):sum of camY that satisfies the condition:Y _(min)<=camY<Y_(max)

Ycount1:count of pixels where camY<Y _(min),

Ycount2:count of pixels where camY>=Y _(max)

Here, Ycount1 may represent the number of underexposed pixels andYcount2 may represent the number of overexposed pixels. This may be usedto determine whether the image is overexposed or underexposed. Forinstance, if the pixels do not saturate, the sum of camY (Y−_(sum)) mayindicate average luma in a scene, which may be used to achieve a targetAE exposure. For instance, in one embodiment, the average luma may bedetermined by dividing Y_(sum) by the number of pixels. Further, byknowing the luma/AE statistics for tile statistics and window locations,AE metering may be performed. For instance, depending on the imagescene, it may be desirable to weigh AE statistics at the center windowmore heavily than those at the edges of the image, such as may be in thecase of a portrait.

In the presently illustrated embodiment, the 3A statistics collectionlogic may be configured to collect statistics in tiles and windows. Inthe illustrated configuration, one window may be defined for tilestatistics 863. The window may be specified with a column start andwidth, and a row start and height. In one embodiment, the windowposition and size may be selected as a multiple of four pixels and,within this window, statistics are gathered in tiles of arbitrary sizes.By way of example, all tiles in the window may be selected such thatthey have the same size. The tile size may be set independently forhorizontal and vertical directions and, in one embodiment, the maximumlimit on the number of horizontal tiles may be set (e.g., a limit of 128horizontal tiles). Further, in one embodiment, the minimum tile size maybe set to 8 pixels wide by 4 pixels high, for example. Below are someexamples of tile configurations based on different video/imaging modesand standards to obtain a window of 16×16 tiles:

-   -   VGA 640×480: the interval 40×30 pixels    -   HD1280×720: the interval 80×45 pixels    -   HD1920×1080: the interval 120×68 pixels    -   5 MP 2592×1944: the interval 162×122 pixels    -   8 MP 3280×2464: the interval 205×154 pixels

With regard to the present embodiment, from the eight available pixelfilters 824 (PF0-PF7), four may be selected for tile statistics 863. Foreach tile, the following statistics may collected:

(R _(sum0) ,G _(sum0) ,B _(sum0))or(sR _(linear) _(—) _(sum0) ,sG_(linear) _(—) _(sum0) ,sB _(linear) _(—) _(sum0)),or

(sR _(sum0) ,sG _(sum0) ,sB _(sum0))or(Y _(sum0) ,C1_(sum0),C2_(sum0)),Count0

(R _(sum1) ,G _(sum1) ,B _(sum1))or(sR _(linear) _(—) _(sum1) ,sG_(linear) _(—) _(sum1),sB_(linear) _(—) _(sum1)),or

(sR _(sum1) ,sG _(sum1) ,sB _(sum1))or(Y _(sum1) ,C1_(sum1),C2_(sum1)),Count1

(R _(sum2) ,G _(sum2) ,B _(sum2))or(sB _(linear) _(—) _(sum2) ,sG_(linear) _(—) _(sum2) ,sB _(linear) _(—) _(sum2)),or

(R _(sum3) ,G _(sum3) ,B _(sum3))or(sB _(linear) _(—) _(sum3) ,sG_(linear) _(—) _(sum3) ,sB _(linear) _(—) _(sum3)),or

(sR _(sum3) ,sG _(sum3) ,sB _(sum3))or(Y _(sum3) ,C1_(sum3),C2_(sum3)),Count3,or

Y _(sum) ,cond(Y _(sum)),Y _(count1) ,Y _(count2)(from camY)

In the above-listed statistics, Count0-3 represents the count of pixelsthat satisfy pixel conditions corresponding to the selected four pixelfilters. For example, if pixel filters PF0, PF1, PF5, and PF6 areselected as the four pixel filters for a particular tile or window, thenthe above-provided expressions may correspond to the Count values andsums corresponding to the pixel data (e.g., Bayer RGB, sRGB_(linear),sRGB, YC1Y2, camYC1C2) which is selected for those filters (e.g., byselection logic 825). Additionally, the Count values may be used tonormalize the statistics (e.g., by dividing color sums by thecorresponding Count values). As shown, depending at least partially uponthe types of statistics needed, the selected pixels filters 824 may beconfigured to select between either one of Bayer RGB, sRGB_(linear), orsRGB pixel data, or YC1C2 (non-linear or camera color space conversiondepending on selection by logic 826) pixel data, and determine color sumstatistics for the selected pixel data. Additionally, as discussed abovethe luma value, camY, from the camera color space conversion (camYC1C2)is also collected for luma sum information for auto-exposure (AE)statistics.

Additionally, the 3A statistics logic 742 may also be configured tocollect statistics 861 for multiple windows. For instance, in oneembodiment, up to eight floating windows may be used, with anyrectangular region having a multiple of four pixels in each dimension(e.g., height×width), up to a maximum size corresponding to the size ofthe image frame. However, the location of the windows is not necessarilyrestricted to multiples of four pixels. For instance, windows canoverlap with one another.

In the present embodiment, four pixel filters 824 may be selected fromthe available eight pixel filters (PF0-PF7) for each window. Statisticsfor each window may be collected in the same manner as for tiles,discussed above. Thus, for each window, the following statistics 861 maybe collected:

(R _(sum0) ,G _(sum0) ,B _(sum0))or sR_(linear) _(—) _(sum0) ,sG_(linear) _(—) _(sum0) ,sB _(linear) _(—) _(sum0)),or

(sR _(sum0) ,sG _(sum0) ,sB _(sum0))or(Y _(sum0) ,C1_(sum0),C2_(sum0)),Count0

(R _(sum1) ,G _(sum1) ,B _(sum1))or(sR _(linear) _(—) _(sum1) ,sG_(linear) _(—) _(sum1) ,sB _(linear) _(—) _(sum1)),or

(sR _(sum1) ,sG _(sum1) ,sB _(sum1))or(Y _(sum1) ,C1_(sum1),C2_(sum1)),Count1

(R _(sum2) ,G _(sum2) ,B _(sum2))or(sB _(linear) _(—) _(sum2) ,sG_(linear) _(—) _(sum2) ,sB _(linear) _(—) _(sum2)),or

(sR _(sum2) ,sG _(sum2) ,sB _(sum2))or(Y _(sum2) ,C1_(sum2),C2_(sum2)),Count2

(R _(sum3) ,G _(sum3) ,B _(sum3))or(sB _(linear) _(—) _(sum3) ,sG_(linear) _(—) _(sum3) ,sB _(linear) _(—) _(sum3)),or

(sR _(sum3) ,sG _(sum3) ,sB _(sum3))or(Y _(sum3) ,C1_(sum3),C2_(sum3)),Count3,or

Y _(sum),cond(Y _(sum)),Y _(count1) ,Y _(count2)(from camY)

In the above-listed statistics, Count0-3 represents the count of pixelsthat satisfy pixel conditions corresponding to the selected four pixelfilters for a particular window. From the eight available pixel filtersPF0-PF7, the four active pixel filters may be selected independently foreach window. Additionally, one of the sets of statistics may becollected using pixel filters or the camY luma statistics. The windowstatistics collected for AWB and AE may, in one embodiment, be mapped toone or more registers.

Referring still to FIG. 82, the 3A statistics logic 742 may also beconfigured to acquire luma row sum statistics 859 for one window usingthe luma value, camY, for the camera color space conversion. Thisinformation may be used to detect and compensate for flicker. Flicker isgenerated by a periodic variation in some fluorescent and incandescentlight sources, typically caused by the AC power signal. For example,referring to FIG. 90, a graph illustrating how flicker may be caused byvariations in a light source is shown. Flicker detection may thus beused to detect the frequency of the AC power used for the light source(e.g., 50 Hz or 60 Hz). Once the frequency is known, flicker may beavoided by setting the image sensor's integration time to an integermultiple of the flicker period.

To detect for flicker, the camera luma, camY, is accumulated over eachrow. Due to the down-sample of the incoming Bayer data, each camY valuemay corresponds to 4 rows of the original raw image data. Control logicand/or firmware may then perform a frequency analysis of the row averageor, more reliably, of the row average differences over consecutiveframes to determine the frequency of the AC power associated with aparticular light source. For example, with respect to FIG. 90,integration times for the image sensor may be based on times t1, t2, t3,and t4 (e.g., such that integration occurs at times corresponding towhen a lighting source exhibiting variations is generally at the samebrightness level.

In one embodiment, a luma row sum window may be specified and statistics859 are reported for pixels within that window. By way of example, for1080p HD video capture, assuming a window of 1024 pixel high, 256 lumarow sums are generated (e.g., one sum for every four rows due todownscaling by logic 795), and each accumulated value may be expressedwith 18 bits (e.g., 8-bit camY values for up to 1024 samples per row).

The 3A statistics collection logic 742 of FIG. 82 may also provide forthe collection of auto-focus (AF) statistics 842 by way of theauto-focus statistics logic 841. A functional block diagram showing anembodiment of the AF statistics logic 841 in more detail is provided inFIG. 91. As shown, the AF statistics logic 841 may include a horizontalfilter 843 and an edge detector 844 which is applied to the originalBayer RGB (not down-sampled), two 3×3 filters 846 on Y from Bayer, andtwo 3×3 filters 847 on camY. In general, the horizontal filter 843provides a fine resolution statistics per color component, the 3×3filters 846 may provide fine resolution statistics on BayerY (Bayer RGBwith 3×1 transform (logic 845) applied), and the 3×3 filters 847 mayprovide coarser two-dimensional statistics on camY (since camY isobtained using down-scaled Bayer RGB data, i.e., logic 815). Further,the logic 841 may include logic 852 for decimating the Bayer RGB data(e.g., 2×2 averaging, 4×4 averaging, etc.), and the decimated Bayer RGBdata 853 may be filtered using 3×3 filters 854 to produce a filteredoutput 855 for decimated Bayer RGB data. The present embodiment providesfor 16 windows of statistics. At the raw frame boundaries, edge pixelsare replicated for the filters of the AF statistics logic 841. Thevarious components of the AF statistics logic 841 are described infurther detail below.

First, the horizontal edge detection process includes applying thehorizontal filter 843 for each color component (R, Gr, Gb, B) followedby an optional edge detector 844 on each color component. Thus,depending on imaging conditions, this configuration allows for the AFstatistic logic 841 to be set up as a high pass filter with no edgedetection (e.g., edge detector disabled) or, alternatively, as a lowpass filter followed by an edge detector (e.g., edge detector enabled).For instance, in low light conditions, the horizontal filter 843 may bemore susceptible to noise and, therefore, the logic 841 may configurethe horizontal filter as a low pass filter followed by an enabled edgedetector 844. As shown, the control signal 848 may enable or disable theedge detector 844. The statistics from the different color channels areused to determine the direction of the focus to improve sharpness, sincethe different colors may focus at different depth. In particular, the AFstatistics logic 841 may provide for techniques to enabling auto-focuscontrol using a combination of coarse and fine adjustments (e.g., to thefocal length of the lens). Embodiments of such techniques are describedin additional detail below.

In one embodiment the horizontal filter may be a 7-tap filter and may bedefined as follows in Equations 41 and 42:

out(i)=(af_horzfilt_coeff [0]*(in(1−3)+in(i+3))+of horzfilt_coeff[1]*(in(1−2)+in(i+2))+af_horzfilt_coeff[2]*(in(1−1)+in(i+1))+af_horzfilt_coeff [3]*in(i))  (41)

out(i)=max(−255,min(255,out(i)))  (42)

Here, each coefficient af_horzfilt_coeff [0:3] may be in the range [−2,2], and i represents the input pixel index for R, Gr, Gb or B. Thefiltered output out(i) may be clipped between a minimum and maximumvalue of −255 and 255, respectively (Equation 42). The filtercoefficients may be defined independently per color component.

The optional edge detector 844 may follow the output of the horizontalfilter 843. In one embodiment, the edge detector 844 may be defined as:

edge(i)=abs(−2*out(i−1)+2*out(i+1))+abs(−out(i−2)+out(i+2))  (43)

edge(i)=max(0,min(255,edge(i)))  (44)

Thus, the edge detector 844, when enabled, may output a value based uponthe two pixels on each side of the current input pixel i, as depicted byEquation 43. The result may be clipped to an 8-bit value between 0 and255, as shown in Equation 44.

Depending on whether an edge is detected, the final output of the pixelfilter (e.g., filter 843 and detector 844) may be selected as either theoutput of the horizontal filter 843 or the output of the edge detector844. For instance, as shown in Equation 45, the output 849 of the edgedetector 844 may be edge(i) if an edge is detected, or may be theabsolute value of the horizontal filter output out(i) if no edge isdetected.

edge(i)=(af_horzfilt_edge_detected)?edge(i):abs(out(i))  (45)

For each window the accumulated values, edge sum[R, Gr, Gb, B], may beselected to be either (1) the sum of edge(j,i) for each pixel over thewindow, or (2) the maximum value of edge(i) across a line in the window,max(edge), summed over the lines in the window. Assuming a raw framesize of 4096×4096 pixels, the number of bits required to store themaximum values of edge sum[R, Gr, Gb, B] is 30 bits (e.g., 8 bits perpixel, plus 22 bits for a window covering the entire raw image frame).

As discussed, the 3×3 filters 847 for camY luma may include twoprogrammable 3×3 filters, referred to as F0 and F1, which are applied tocamY. The result of the filter 847 goes to either a squared function oran absolute value function. The result is accumulated over a given AFwindow for both 3×3 filters F0 and F1 to generate a luma edge value. Inone embodiment, the luma edge values at each camY pixel are defined asfollows:

$\begin{matrix}{{{edgecamY\_ FX}\left( {j,i} \right)} = {{{FX}*{camY}} = {{{{FX}\left( {0,0} \right)}*{{camY}\left( {{j - 1},{i - 1}} \right)}} + {{{FX}\left( {0,1} \right)}*{{camY}\left( {{j - 1},i} \right)}} + {{{FX}\left( {0,2} \right)}*{{camY}\left( {{j - 1},{i + 1}} \right)}} + {{{FX}\left( {1,0} \right)}*{{camY}\left( {j,{i - 1}} \right)}} + {{{FX}\left( {1,1} \right)}*{{camY}\left( {j,i} \right)}} + {{{FX}\left( {1,2} \right)}*{{camY}\left( {j,{i + 1}} \right)}} + {{{FX}\left( {2,0} \right)}*{{camY}\left( {{j + 1},{i - 1}} \right)}} + {{{FX}\left( {2,1} \right)}*{{camY}\left( {{j + 1},i} \right)}} + {{{FX}\left( {2,2} \right)}*{{camY}\left( {{j + 1},{i + 1}} \right)}}}}} & (46) \\{{{{edgecamY\_ FX}\left( {j,i} \right)} = {f\left( {\max \left( {{- 255},{\min \left( {255,{{edgecamY\_ FX}\left( {j,i} \right)}} \right)}} \right)} \right)}}\mspace{20mu} {{f(a)} = {{a\hat{}2}\mspace{14mu} {or}\mspace{14mu} {{abs}(a)}}}} & (47)\end{matrix}$

where FX represents the 3×3 programmable filters, F0 and F1, with signedcoefficients in the range [−4, 4]. The indices j and i represent pixellocations in the camY image. As discussed above, the filter on camY mayprovide coarse resolution statistics, since camY is derived usingdown-scaled (e.g., 4×4 to 1) Bayer RGB data. For instance, in oneembodiment, the filters F0 and F1 may be set using a Scharr operator,which offers improved rotational symmetry over a Sobel operator, anexample of which is shown below:

${F\; 0} = {{\begin{bmatrix}{- 3} & 0 & 3 \\{- 10} & 0 & 10 \\{- 3} & 0 & 3\end{bmatrix}\mspace{14mu} F\; 1} = \begin{bmatrix}{- 3} & {- 10} & {- 3} \\0 & 0 & 0 \\3 & 10 & 3\end{bmatrix}}$

For each window, the accumulated values 850 determined by the filters847, edgecamY_FX_sum (where FX═F0 and F1), can selected to be either (1)the sum of edgecamY_FX(j,i) for each pixel over the window, or (2) themaximum value of edgecamY_FX(j) across a line in the window, summed overthe lines in the window. In one embodiment, edgecamY_FX_sum may saturateto a 32-bit value when f(a) is set to â2 to provide “peakier” statisticswith a finer resolution. To avoid saturation, a maximum window size X*Yin raw frame pixels may be set such that it does not exceed a total of1024×1024 pixels (e.g., i.e. X*Y<=1048576 pixels). As noted above, f(a)may also be set as an absolute value to provide more linear statistics.

The AF 3×3 filters 846 on Bayer Y may defined in a similar manner as the3×3 filters in camY, but they are applied to luma values Y generatedfrom a Bayer quad (2×2 pixels). First, 8-bit Bayer RGB values areconverted to Y with programmable coefficients in the range [0, 4] togenerate a white balanced Y value, as shown below in Equation 48:

bayerY=max(0,min(255,bayerY_Coeff [0]*R+bayerY_Coeff[1]*(Gr+Gb)/2+bayerY_Coeff [2]*B))  (48)

Like the filters 847 for camY, the 3×3 filters 846 for bayerY luma mayinclude two programmable 3×3 filters, referred to as F0 and F1, whichare applied to bayerY. The result of the filter 846 goes to either asquared function or an absolute value function. The result isaccumulated over a given AF window for both 3×3 filters F0 and F1 togenerate a luma edge value. In one embodiment, the luma edge values ateach bayerY pixel are defined as follows:

$\begin{matrix}{{{edgebayerY\_ FX}\left( {j,i} \right)} = {{{FX}*{bayerY}} = {{{{FX}\left( {0,0} \right)}*{{bayerY}\left( {{j - 1},{i - 1}} \right)}} + {{{FX}\left( {0,1} \right)}*{{bayerY}\left( {{j - 1},i} \right)}} + {{{FX}\left( {0,2} \right)}*{{bayerY}\left( {{j - 1},i} \right)}} + {{{FX}\left( {1,0} \right)}*{{bayerY}\left( {j,{i - 1}} \right)}} + {{{FX}\left( {1,1} \right)}*{{bayerY}\left( {j,i} \right)}} + {{{FX}\left( {1,2} \right)}*{{bayerY}\left( {{j - 1},i} \right)}} + {{{FX}\left( {2,0} \right)}*{{bayerY}\left( {{j + 1},{i - 1}} \right)}} + {{{FX}\left( {2,1} \right)}*{{bayerY}\left( {{j + 1},i} \right)}} + {{{FX}\left( {2,2} \right)}*{{bayerY}\left( {{j + 1},i} \right)}}}}} & (46) \\{{{{edgebayerY\_ FX}\left( {j,i} \right)} = {f\left( {\max \left( {{- 255},{\min \left( {255,{{edgebayerY\_ FX}\left( {j,i} \right)}} \right)}} \right)} \right)}}\mspace{20mu} {{f(a)} = {{a\hat{}2}\mspace{14mu} {or}\mspace{14mu} {{abs}(a)}}}} & (47)\end{matrix}$

where FX represents the 3×3 programmable filters, F0 and F1, with signedcoefficients in the range [−4, 4]. The indices j and i represent pixellocations in the bayerY image. As discussed above, the filter on Bayer Ymay provide fine resolution statistics, since the Bayer RGB signalreceived by the AF logic 841 is not decimated. By way of examples only,the filters F0 and F1 of the filter logic 846 may be set using one ofthe following filter configurations:

${\begin{bmatrix}{- 1} & {- 1} & {- 1} \\{- 1} & 8 & {- 1} \\{- 1} & {- 1} & {- 1}\end{bmatrix}\mspace{14mu}\begin{bmatrix}{- 6} & 10 & 6 \\10 & 0 & {- 10} \\6 & {- 10} & {- 6}\end{bmatrix}}\mspace{14mu}\begin{bmatrix}0 & {- 1} & 0 \\{- 1} & 2 & 0 \\0 & 0 & 0\end{bmatrix}$

For each window, the accumulated values 851 determined by the filters846, edgebayerY_FX_sum (where FX═F0 and F1), can selected to be either(1) the sum of edgebayerY_FX(j,i) for each pixel over the window, or (2)the maximum value of edgebayerY_FX(j) across a line in the window,summed over the lines in the window. Here, edgebayerY_FX_sum maysaturates to 32-bits when f(a) is set to â2. Thus, to avoid saturation,the maximum window size X*Y in raw frame pixels should be set such thatit does not exceed a total of 512×512 pixels (e.g., X*Y<=262144). Asdiscussed above, setting f(a) to â2 may provide for peakier statistics,while setting f(a) to abs(a) may provide for more linear statistics.

As discussed above, statistics 842 for AF are collected for 16 windows.The windows may be any rectangular area with each dimension being amultiple of 4 pixels. Because each filtering logic 846 and 847 includestwo filters, in some instances, one filter may be used for normalizationover 4 pixels, and may be configured to filter in both vertical andhorizontal directions. Further, in some embodiments, the AF logic 841may normalize the AF statistics by brightness. This may be accomplishedby setting one or more of the filters of the logic blocks 846 and 847 asbypass filters. In certain embodiments, the location of the windows maybe restricted to multiple of 4 pixels, and windows are permitted tooverlap. For instance, one window may be used to acquire normalizationvalues, while another window may be used for additional statistics, suchas variance, as discussed below. In one embodiment, the AF filters(e.g., 843, 846, 847) may not implement pixel replication at the edge ofan image frame and, therefore, in order for the AF filters to use allvalid pixels, the AF windows may be set such that they are each at least4 pixels from the top edge of the frame, at least 8 pixels from thebottom edge of the frame and at least 12 pixels from the left/right edgeof the frame. In the illustrated embodiment, the following statisticsmay be collected and reported for each window:

-   -   32-bit edgeGr_sum for Gr    -   32-bit edgeR_sum for R    -   32-bit edgeB_sum for B    -   32-bit edgeGb_sum for Gb    -   32-bit edgebayerY_F0_sum for Y from Bayer for filter0 (F0)    -   32-bit edgebayerY_F1_sum for Y from Bayer for filter1 (F1)    -   32-bit edgecamY_F0_sum for camY for filter0 (F0)    -   32-bit edgecamY_F1_sum for camY for filter1 (F1)        In such an embodiment, the memory required for storing the AF        statistics 842 may be 16 (windows) multiplied by 8 (Gr, R, B,        Gb, bayerY_F0, bayerY_F1, camY_F0, camY_F1) multiplied by 32        bits.

Thus, in one embodiment, the accumulated value per window may beselected between: the output of the filter (which may be configured as adefault setting), the input pixel, or the input pixel squared. Theselection may be made for each of the 16 AF windows, and may apply toall of the 8 AF statistics (listed above) in a given window. This may beused to normalize the AF score between two overlapping windows, one ofwhich is configured to collect the output of the filter and one of whichis configured to collect the input pixel sum. Additionally, forcalculating pixel variance in the case of two overlapping windows, onewindow may be configured to collect the input pixel sum, and another tocollect the input pixel squared sum, thus providing for a variance thatmay be calculated as:

Variance=(avg_pixel²)−(avg_pixel)̂2

Using the AF statistics, the ISP control logic 84 (FIG. 7) may beconfigured to adjust a focal length of the lens of an image device(e.g., 30) using a series of focal length adjustments based on coarseand fine auto-focus “scores” to bring an image into focus. As discussedabove, the 3×3 filters 847 for camY may provide for coarse statistics,while the horizontal filter 843 and edge detector 844 may provide forcomparatively finer statistics per color component, while the 3×3filters 846 on BayerY may provide for fine statistics on BayerY.Further, the 3×3 filters 854 on a decimated Bayer RGB signal 853 mayprovide coarse statistics for each color channel. As discussed furtherbelow, AF scores may be calculated based on filter output values for aparticular input signal (e.g., sum of filter outputs F0 and F1 for camY,BayerY, Bayer RGB decimated, or based on horizontal/edge detectoroutputs, etc.).

FIG. 92 shows a graph 856 that depicts curves 858 and 860 whichrepresent coarse and fine AF scores, respectively. As shown, the coarseAF scores based upon the coarse statistics may have a more linearresponse across the focal distance of the lens. Thus, at any focalposition, a lens movement may generate a change in an auto focus scorewhich may be used to detect if the image is becoming more in focus orout of focus. For instance, an increase in a coarse AF score after alens adjustment may indicate that the focal length is being adjusted inthe correct direction (e.g., towards the optical focal position).

However, as the optical focal position is approached, the change in thecoarse AF score for smaller lens adjustments steps may decrease, makingit difficult to discern the correct direction of focal adjustment. Forexample, as shown on graph 856, the change in coarse AF score betweencoarse position (CP) CP1 and CP2 is represented by Δ_(C12), which showsan increase in the coarse from CP1 to CP2. However, as shown, from CP3to CP4, the change Δ_(C34) in the coarse AF score (which passes throughthe optimal focal position (OFP)), though still increasing, isrelatively smaller. It should be understood that the positions CP1-CP6along the focal length L are not meant to necessarily correspond to thestep sizes taken by the auto-focus logic along the focal length. Thatis, there may be additional steps taken between each coarse positionthat are not shown. The illustrated positions CP1-CP6 are only meant toshow how the change in the coarse AF score may gradually decrease as thefocal position approaches the OFP.

Once the approximate position of the OFP is determined (e.g., based onthe coarse AF scores shown in FIG. 92, the approximate position of theOFP may be between CP3 and CP5), fine AF score values, represented bycurve 860 may be evaluated to refine the focal position. For instance,fine AF scores may be flatter when the image is out of focus, so that alarge lens positional change does not cause a large change in the fineAF score. However, as the focal position approaches the optical focalposition (OFP), the fine AF score may change sharply with smallpositional adjustments. Thus, by locating a peak or apex 862 on the fineAF score curve 860, the OFP may be determined for the current imagescene. Thus, to summarize, coarse AF scores may be used to determine thegeneral vicinity of the optical focal position, while the fine AF scoresmay be used to pinpoints a more exact position within that vicinity.

In one embodiment, the auto-focus process may begin by acquiring coarseAF scores along the entire available focal length, beginning at position0 and ending at position L (shown on graph 856) and determine the coarseAF scores at various step positions (e.g., CP1-CP6). In one embodiment,once the focal position of the lens has reached position L, the positionmay reset to 0 before evaluating AF scores at various focal positions.For instance, this may be due to coil settling time of a mechanicalelement controlling the focal position. In this embodiment, afterresetting to position 0, the focal position may be adjusted towardposition L to a position that first indicated a negative change in acoarse AF score, here position CP5 exhibiting a negative change Δ_(C45)with respect to position CP4. From position CP5, the focal position maybe adjusted in smaller increments relative to increments used in thecoarse AF score adjustments (e.g., positions FP1, FP2, FP3, etc.) backin the direction towards position 0, while searching for a peak 862 inthe fine AF score curve 860. As discussed above, the focal position OFPcorresponding to the peak 862 in the fine AF score curve 860 may be theoptimal focal position for the current image scene.

As will be appreciated, the techniques described above for locating theoptimal area and optimal position for focus may be referred to as “hillclimbing,” in the sense that the changes in the curves for the AF scores858 and 860 are analyzed to locate the OFP. Further, while the analysisof the coarse AF scores (curve 858) and the fine AF scores (curve 860)is shown as using same-sized steps for coarse score analysis (e.g.,distance between CP1 and CP2) and same-sized steps for fine scoreanalysis (e.g., distance between FP1 and FP2), in some embodiments, thestep sizes may be varied depending on the change in the score from oneposition to the next. For instance, in one embodiment, the step sizebetween CP3 and CP4 may be reduced relative to the step size between CP1and CP2 since the overall delta in the coarse AF score (Δ_(C34)) is lessthen the delta from CP1 to CP2 (Δ_(C12)).

A method 864 depicting this process is illustrated in FIG. 93. Beginningat block 865, a coarse AF score is determined for image data at varioussteps along the focal length, from position 0 to position L (FIG. 92).Thereafter, at block 866, the coarse AF scores are analyzed and thecoarse position exhibiting the first negative change in the coarse AFscore is identified as a starting point for fine AF scoring analysis.For instance, subsequently, at block 867, the focal position is steppedback towards the initial position 0 at smaller steps, with the fine AFscore at each step being analyzed until a peak in the AF score curve(e.g., curve 860 of FIG. 92) is located. At block 868, the focalposition corresponding to the peak is set as the optimal focal positionfor the current image scene.

As discussed above, due to mechanical coil settling times, theembodiment of the technique shown in FIG. 93 may be adapted to acquirecoarse AF scores along the entire focal length initially, rather thananalyzing each coarse position one by one and searching for an optimalfocus area. Other embodiments, however, in which coil settling times areless of a concern, may analyze coarse AF scores one by one at each step,instead of searching the entire focal length.

In certain embodiments, the AF scores may be determined using whitebalanced luma values derived from Bayer RGB data. For instance, the lumavalue, Y, may be derived by decimating a 2×2 Bayer quad by a factor of2, as shown in FIG. 94, or by decimating a 4×4 pixel block consisting offour 2×2 Bayer quads by a factor of 4, as shown in FIG. 95. In oneembodiment, AF scores may be determined using gradients. In anotherembodiment, AF scores may be determined by applying a 3×3 transformusing a Scharr operator, which provides rotational symmetry whileminimizing weighted mean squared angular errors in the Fourier domain.By way of example, the calculation of a coarse AF score on camY using acommon Scharr operator (discussed above) is shown below:

${{AFScore}_{coarse} = {{f\left( {\begin{bmatrix}{- 3} & 0 & 3 \\{- 10} & 0 & 10 \\{- 3} & 0 & 3\end{bmatrix} \times {in}} \right)} + {f\left( {\begin{bmatrix}{- 3} & {- 10} & {- 3} \\0 & 0 & 0 \\3 & 10 & 3\end{bmatrix} \times {in}} \right)}}},$

where in represents the decimated luma Y value. In other embodiments,the AF score for both coarse and fine statistics may be calculated usingother 3×3 transforms.

Auto focus adjustments may also be performed differently depending onthe color components, since different wavelengths of light may beaffected differently by the lens, which is one reason the horizontalfilter 843 is applied to each color component independently. Thus,auto-focus may still be performed even in the present of chromaticaberration in the lens. For instance, because red and blue typicallyfocuses at a different position or distance with respect to green whenchromatic aberrations are present, relative AF scores for each color maybe used to determine the direction to focus. This is better illustratedin FIG. 96, which shows the optimal focal position for blue, red, andgreen color channels for a lens 870. As shown, the optimal focalpositions for red, green, and blue are depicted by reference letters R,G, and B respectively, each corresponding to an AF score, with a currentfocal position 872. Generally, in such a configuration, it may bedesirable to select the optimal focus position as the positioncorresponding to the optimal focal position for green components (e.g.,since Bayer RGB has twice as many green as red or blue components), hereposition G. Thus, it may be expected that for an optimal focal position,the green channel should exhibit the highest auto-focus score. Thus,based on the positions of the optimal focal positions for each color(with those closer to the lens having higher AF scores), the AF logic841 and associated control logic 84 may determine which direction tofocus based on the relative AF scores for blue, green, and red. Forinstance, if the blue channel has a higher AF score relative to thegreen channel (as shown in FIG. 96), then the focal position is adjustedin the negative direction (towards the image sensor) without having tofirst analyze in the positive direction from the current position 872.In some embodiments, illuminant detection or analysis using colorcorrelated temperatures (CCT) may be performed.

Further, as mentioned above, variance scores may also be used. Forinstance, pixel sums and pixel squared sum values may be accumulated forblock sizes (e.g., 8×8-32×32 pixels), and may be used to derive variancescores (e.g., avg_pixel²)−(avg_pixel)̂2). The variances may be summed toget a total variance score for each window. Smaller block sizes may beused to obtain fine variance scores, and larger block sizes may be usedto obtain coarser variance scores.

Referring to the 3A statistics logic 742 of FIG. 82, the logic 742 mayalso be configured to collect component histograms 874 and 876. As willbe appreciated, histograms may be used to analyze the pixel leveldistribution in an image. This may be useful for implementing certainfunctions, such as histogram equalization, where the histogram data isused to determine the histogram specification (histogram matching). Byway of example, luma histograms may be used for AE (e.g., foradjusting/setting sensor integration times), and color histograms may beused for AWB. In the present embodiment, histograms may be 256, 128, 64or 32 bins (where the top 8, 7, 6, and 5 bits of the pixel is used todetermine the bin, respectively) for each color component, as specifiedby a bin size (BinSize). For instance, when pixel data is 14-bits, anadditional scale factor between 0-6 and an offset may be specified todetermine what range (e.g., which 8 bits) of the pixel data is collectedfor statistics purposes. The bin number may be obtained as follows:

idx=((pixel−hist_offset)>>(6−hist_scale)

In one embodiment, the color histogram bins are incremented only if thebin indices are in the range [0, 2̂(8−BinSize)]:

if(idx>=0&&idx<2̂(8−BinSize))

StatsHist[idx]+=Count;

In the present embodiment, the statistics processing unit 142 mayinclude two histogram units. This first histogram 874 (Hist0) may beconfigured to collect pixel data as part of the statistics collectionafter the 4×4 decimation. For Hist0, the components may be selected tobe RGB, sRGB_(linear), sRGB or YC1C2 using selection circuit 880. Thesecond histogram 876 (Hist1) may be configured to collect pixel databefore the statistics pipeline (before defective pixel correction logic738), as shown in more detail in FIG. 96. For instance, the raw BayerRGB data (output from 146) may be decimated (to produce signal 878)using logic 882 by skipping pixels, as discussed further below. For thegreen channel, the color may be selected between Gr, Gb or both Gr andGb (both Gr and Gb counts are accumulated in the Green bins).

In order to keep the histogram bin width the same between the twohistograms, Hist1 may be configured to collect pixel data every 4 pixels(every other Bayer quad). The start of the histogram window determinesthe first Bayer quad location where the histogram starts accumulating.Starting at this location, every other Bayer quad is skippedhorizontally and vertically for Hist1. The window start location can beany pixel position for Hist1 and, therefore pixels being skipped by thehistogram calculation can be selected by changing the start windowlocation. Hist1 can be used to collect data, represented by 884 in FIG.97, close to the black level to assist in dynamic black levelcompensation at block 739. Thus, while shown in FIG. 97 as beingseparate from the 3A statistics logic 742 for illustrative purposes, itshould be understood that the histogram 876 may actually be part of thestatistics written to memory, and may be actually be physically locatedwithin the statistics processing unit 142.

In the present embodiment, the red (R) and blue (B) bins may be 20-bits,with the green (G) bin is 21-bits (Green is larger to accommodate the Grand Gb accumulation in Hist1). This allows for a maximum picture size of4160 by 3120 pixels (12 MP). The internal memory size required is3×256×20(1) bits (3 color components, 256 bins).

With regard to memory format, statistics for AWB/AE windows, AF windows,2D color histogram, and component histograms may be mapped to registersto allow early access by firmware. In one embodiment, two memorypointers may be used to write statistics to memory, one for tilestatistics 863, and one for luma row sums 859, followed by all othercollected statistics. All statistics are written to external memory,which may be DMA memory. The memory address registers may bedouble-buffered so that a new location in memory can be specified onevery frame.

Before proceeding with a detailed discussion of the ISP pipe logic 82downstream from the ISP front-end logic 80, it should understood thatthe arrangement of various functional logic blocks in the statisticsprocessing units 142 and 144 (e.g., logic blocks 738, 739, 740, 741, and742) and the ISP front-end pixel processing unit 150 (e.g., logic blocks650 and 652) are intended to illustrate only one embodiment of thepresent technique. Indeed, in other embodiments, the logic blocksillustrated herein may be arranged in different ordering, or may includeadditional logic blocks that may perform additional image processingfunctions not specifically described herein. Further, it should beunderstood that the image processing operations performed in thestatistics processing units (e.g., 142 and 144), such as lens shadingcorrection, defective pixel detection/correction, and black levelcompensation, are performed within the statistics processing units forthe purposes of collecting statistical data. Thus, processing operationsperformed upon the image data received by the statistical processingunits are not actually reflected in the image signal 109 (FEProcOut)that is output from the ISP front-end pixel processing logic 150 andforwarded to the ISP pipe processing logic 82.

Before continuing, it should also be noted, that given sufficientprocessing time and the similarity between many of the processingrequirements of the various operations described herein, it is possibleto reconfigure the functional blocks shown herein to perform imageprocessing in a sequential manner, rather than a pipe-lined nature. Aswill be understood, this may further reduce the overall hardwareimplementation costs, but may also increase bandwidth to external memory(e.g., to cache/store intermediate results/data).

The ISP Pipeline (“Pipe”) Processing Logic

Having described the ISP front-end logic 80 in detail above, the presentdiscussion will now shift focus to the ISP pipe processing logic 82.Generally, the function of the ISP pipe logic 82 is to receive raw imagedata, which may be provided from the ISP front-end logic 80 or retrievedfrom memory 108, and to perform additional image processing operations,i.e., prior to outputting the image data to the display device 28.

A block diagram showing an embodiment of the ISP pipe logic 82 isdepicted in FIG. 98. As illustrated, the ISP pipe logic 82 may includeraw processing logic 900, RGB processing logic 902, and YCbCr processinglogic 904. The raw processing logic 900 may perform various imageprocessing operations, such as defective pixel detection and correction,lens shading correction, demosaicing, as well as applying gains forauto-white balance and/or setting a black level, as will be discussedfurther below. As shown in the present embodiment, the input signal 908to the raw processing logic 900 may be the raw pixel output 109 (signalFEProcOut) from the ISP front-end logic 80 or the raw pixel data 112from the memory 108, depending on the present configuration of theselection logic 906.

As a result of demosaicing operations performed within the rawprocessing logic 900, the image signal output 910 may be in the RGBdomain, and may be subsequently forwarded to the RGB processing logic902. For instance, as shown in FIG. 98, the RGB processing logic 902receives the signal 916, which may be the output signal 910 or an RGBimage signal 912 from the memory 108, depending on the presentconfiguration of the selection logic 914. The RGB processing logic 902may provide for various RGB color adjustment operations, including colorcorrection (e.g., using a color correction matrix), the application ofcolor gains for auto-white balancing, as well as global tone mapping, aswill be discussed further below. The RGB processing logic 904 may alsoprovide for the color space conversion of RGB image data to the YCbCr(luma/chroma) color space. Thus, the image signal output 918 may be inthe YCbCr domain, and may be subsequently forwarded to the YCbCrprocessing logic 904.

For instance, as shown in FIG. 98, the YCbCr processing logic 904receives the signal 924, which may be the output signal 918 from the RGBprocessing logic 902 or a YCbCr signal 920 from the memory 108,depending on the present configuration of the selection logic 922. Aswill be discussed in further detail below, the YCbCr processing logic904 may provide for image processing operations in the YCbCr colorspace, including scaling, chroma suppression, luma sharpening,brightness, contrast, and color (BCC) adjustments, YCbCr gamma mapping,chroma decimation, and so forth. The image signal output 926 of theYCbCr processing logic 904 may be sent to the memory 108, or may beoutput from the ISP pipe processing logic 82 as the image signal 114(FIG. 7). Next, in accordance with the embodiment of the imageprocessing circuitry 32 depicted in FIG. 7, the image signal 114 may besent to the display device 28 (either directly or via memory 108) forviewing by the user, or may be further processed using a compressionengine (e.g., encoder 118), a CPU/GPU, a graphics engine, or the like.Additionally, in an embodiment where an ISP back-end unit 120 isincluded in the image processing circuitry 32 (e.g., FIG. 8), the imagesignal 114 may be sent to the ISP back-end processing logic 120 foradditional down-stream post-processing.

In accordance with embodiments of the present techniques, the ISP pipelogic 82 may support the processing of raw pixel data in 8-bit, 10-bit,12-bit, or 14-bit formats. For instance, in one embodiment, 8-bit,10-bit, or 12-bit input data may be converted to 14-bit at the input ofthe raw processing logic 900, and raw processing and RGB processingoperations may be performed with 14-bit precision. In the latterembodiment, the 14-bit image data may be down-sampled to 10 bits priorto the conversion of the RGB data to the YCbCr color space, and theYCbCr processing (logic 904) may be performed with 10-bit precision.

In order to provide a comprehensive description of the various functionsprovided by the ISP pipe processing logic 82, each of the raw processinglogic 900, RGB processing logic 902, and YCbCr processing logic 904, aswell as internal logic for performing various image processingoperations that may be implemented in each respective unit of logic 900,902, and 904, will be discussed sequentially below, beginning with theraw processing logic 900. For instance, referring now to FIG. 99, ablock diagram showing a more detailed view of an embodiment of the rawprocessing logic 900 is illustrated, in accordance with an embodiment ofthe present technique. As shown, the raw processing logic 900 includesthe gain, offset, and clamping (GOC) logic 930, defective pixeldetection/correction (DPDC) logic 932, the noise reduction logic 934,lens shading correction logic 936, GOC logic 938, and demosaicing logic940. Further, while the examples discussed below assume the use of aBayer color filter array with the image sensor(s) 90, it should beunderstood that other embodiments of the present technique may utilizedifferent types of color filters as well.

The input signal 908, which may be a raw image signal, is first receivedby the gain, offset, and clamping (GOC) logic 930. The GOC logic 930 mayprovide similar functions and may be implemented in a similar mannerwith respect to the BLC logic 739 of the statistics processing unit 142of the ISP front-end logic 80, as discussed above in FIG. 68. Forinstance, the GOC logic 930 may provide digital gain, offsets andclamping (clipping) independently for each color component R, B, Gr, andGb of a Bayer image sensor. Particularly, the GOC logic 930 may performauto-white balance or set the black level of the raw image data.Further, in some embodiments, the GOC logic 930 may also be used corrector compensate for an offset between the Gr and Gb color components.

In operation, the input value for the current pixel is first offset by asigned value and multiplied by a gain. This operation may be performedusing the formula shown in Equation 11 above, wherein X represents theinput pixel value for a given color component R, B, Gr, or Gb, O[c]represents a signed 16-bit offset for the current color component c, andG[c] represents a gain value for the color component c. The values forG[c] may be previously determined during statistics processing (e.g., inthe ISP front-end block 80). In one embodiment, the gain G[c] may be a16-bit unsigned number with 2 integer bits and 14 fraction bits (e.g.,2.14 floating point representation), and the gain G[c] may be appliedwith rounding. By way of example only, the gain G[c] may have a range ofbetween 0 to 4×.

The computed pixel value Y (which includes the gain G[c] and offsetO[c]) from Equation 11 is then be clipped to a minimum and a maximumrange in accordance with Equation 12. As discussed above, the variablesmin[c] and max[c] may represent signed 16-bit “clipping values” for theminimum and maximum output values, respectively. In one embodiment, theGOC logic 930 may also be configured to maintain a count of the numberof pixels that were clipped above and below maximum and minimum ranges,respectively, for each color component.

Subsequently, the output of the GOC logic 930 is forwarded to thedefective pixel detection and correction logic 932. As discussed abovewith reference to FIG. 68 (DPDC logic 738), defective pixels mayattributable to a number of factors, and may include “hot” (or leaky)pixels, “stuck” pixels, and “dead pixels, wherein hot pixels exhibit ahigher than normal charge leakage relative to non-defective pixels, andthus may appear brighter than non-defective pixel, and wherein a stuckpixel appears as always being on (e.g., fully charged) and thus appearsbrighter, whereas a dead pixel appears as always being off. As such, itmay be desirable to have a pixel detection scheme that is robust enoughto identify and address different types of failure scenarios.Particularly, when compared to the front-end DPDC logic 738, which mayprovide only dynamic defect detection/correction, the pipe DPDC logic932 may provide for fixed or static defect detection/correction, dynamicdefect detection/correction, as well as speckle removal.

In accordance with embodiments of the presently disclosed techniques,defective pixel correction/detection performed by the DPDC logic 932 mayoccur independently for each color component (e.g., R, B, Gr, and Gb),and may include various operations for detecting defective pixels, aswell as for correcting the detected defective pixels. For instance, inone embodiment, the defective pixel detection operations may provide forthe detection of static defects, dynamics defects, as well as thedetection of speckle, which may refer to the electrical interferences ornoise (e.g., photon noise) that may be present in the imaging sensor. Byanalogy, speckle may appear on an image as seemingly random noiseartifacts, similar to the manner in which static may appear on adisplay, such as a television display. Further, as noted above, dynamicdefection correction is regarded as being dynamic in the sense that thecharacterization of a pixel as being defective at a given time maydepend on the image data in the neighboring pixels. For example, a stuckpixel that is always on maximum brightness may not be regarded as adefective pixel if the location of the stuck pixel is in an area of thecurrent image that is dominate by bright white colors. Conversely, ifthe stuck pixel is in a region of the current image that is dominated byblack or darker colors, then the stuck pixel may be identified as adefective pixel during processing by the DPDC logic 932 and correctedaccordingly.

With regard to static defect detection, the location of each pixel iscompared to a static defect table, which may store data corresponding tothe location of pixels that are known to be defective. For instance, inone embodiment, the DPDC logic 932 may monitor the detection ofdefective pixels (e.g., using a counter mechanism or register) and, if aparticular pixel is observed as repeatedly failing, the location of thatpixel is stored into the static defect table. Thus, during static defectdetection, if it is determined that the location of the current pixel isin the static defect table, then the current pixel is identified asbeing a defective pixel, and a replacement value is determined andtemporarily stored. In one embodiment, the replacement value may be thevalue of the previous pixel (based on scan order) of the same colorcomponent. The replacement value may be used to correct the staticdefect during dynamic/speckle defect detection and correction, as willbe discussed below. Additionally, if the previous pixel is outside ofthe raw frame 310 (FIG. 23), then its value is not used, and the staticdefect may be corrected during the dynamic defect correction process.Further, due to memory considerations, the static defect table may storea finite number of location entries. For instance, in one embodiment,the static defect table may be implemented as a FIFO queue configured tostore a total of 16 locations for every two lines of image data. Thelocations in defined in the static defect table will, nonetheless, becorrected using a previous pixel replacement value (rather than via thedynamic defect detection process discussed below). As mentioned above,embodiments of the present technique may also provide for updating thestatic defect table intermittently over time.

Embodiments may provide for the static defect table to be implemented inon-chip memory or off-chip memory. As will be appreciated, using anon-chip implementation may increase overall chip area/size, while usingan off-chip implementation may reduce chip area/size, but increasememory bandwidth requirements. Thus, it should be understood that thestatic defect table may be implemented either on-chip or off-chipdepending on specific implementation requirements, i.e., the totalnumber of pixels that are to be stored within the static defect table.

The dynamic defect and speckle detection processes may be time-shiftedwith respect to the static defect detection process discussed above. Forinstance, in one embodiment, the dynamic defect and speckle detectionprocess may begin after the static defect detection process has analyzedtwo scan lines (e.g., rows) of pixels. As can be appreciated, thisallows for the identification of static defects and their respectivereplacement values to be determined before dynamic/speckle detectionoccurs. For example, during the dynamic/speckle detection process, ifthe current pixel was previously marked as being a static defect, ratherthan applying dynamic/speckle detection operations, the static defect issimply corrected using the previously assessed replacement value.

With regard to dynamic defect and speckle detection, these processes mayoccur sequentially or in parallel. The dynamic defect and speckledetection and correction that is performed by the DPDC logic 932 mayrely on adaptive edge detection using pixel-to-pixel directiongradients. In one embodiment, the DPDC logic 932 may select the eightimmediate neighbors of the current pixel having the same color componentthat are within the raw frame 310 (FIG. 23) are used. In other words,the current pixels and its eight immediate neighbors P0, P1, P2, P3, P4,P5, P6, and P7 may form a 3×3 area, as shown below in FIG. 100.

It should be noted, however, that depending on the location of thecurrent pixel P, pixels outside the raw frame 310 are not consideredwhen calculating pixel-to-pixel gradients. For example, with regard tothe “top-left” case 942 shown in FIG. 100, the current pixel P is at thetop-left corner of the raw frame 310 and, thus, the neighboring pixelsP0, P1, P2, P3, and P5 outside of the raw frame 310 are not considered,leaving only the pixels P4, P6, and P7 (N=3). In the “top” case 944, thecurrent pixel P is at the top-most edge of the raw frame 310 and, thus,the neighboring pixels P0, P1, and P2 outside of the raw frame 310 arenot considered, leaving only the pixels P3, P4, P5, P6, and P7 (N=5).Next, in the “top-right” case 946, the current pixel P is at thetop-right corner of the raw frame 310 and, thus, the neighboring pixelsP0, P1, P2, P4, and P7 outside of the raw frame 310 are not considered,leaving only the pixels P3, P5, and P6 (N=3). In the “left” case 948,the current pixel P is at the left-most edge of the raw frame 310 and,thus, the neighboring pixels P0, P3, and P5 outside of the raw frame 310are not considered, leaving only the pixels P1, P2, P4, P6, and P7(N=5).

In the “center” case 950, all pixels P0-P7 lie within the raw frame 310and are thus used in determining the pixel-to-pixel gradients (N=8). Inthe “right” case 952, the current pixel P is at the right-most edge ofthe raw frame 310 and, thus, the neighboring pixels P2, P4, and P7outside of the raw frame 310 are not considered, leaving only the pixelsP0, P1, P3, P5, and P6 (N=5). Additionally, in the “bottom-left” case954, the current pixel P is at the bottom-left corner of the raw frame310 and, thus, the neighboring pixels P0, P3, P5, P6, and P7 outside ofthe raw frame 310 are not considered, leaving only the pixels P1, P2,and P4 (N=3). In the “bottom” case 956, the current pixel P is at thebottom-most edge of the raw frame 310 and, thus, the neighboring pixelsP5, P6, and P7 outside of the raw frame 310 are not considered, leavingonly the pixels P0, P1, P2, P3, and P4 (N=5). Finally, in the“bottom-right” case 958, the current pixel P is at the bottom-rightcorner of the raw frame 310 and, thus, the neighboring pixels P2, P4,P5, P6, and P7 outside of the raw frame 310 are not considered, leavingonly the pixels P0, P1, and P3 (N=3).

Thus, depending upon the position of the current pixel P, the number ofpixels used in determining the pixel-to-pixel gradients may be 3, 5, or8. In the illustrated embodiment, for each neighboring pixel (k=0 to 7)within the picture boundary (e.g., raw frame 310), the pixel-to-pixelgradients may be calculated as follows:

G _(k)=abs(P−P _(k)),for 0≦k≦7(only for k within the raw frame)  (51)

Additionally, an average gradient, G_(av), may be calculated as thedifference between the current pixel and the average, P_(av), of itssurrounding pixels, as shown by the equations below:

$\begin{matrix}{{P_{av} = \frac{\left( {\sum\limits_{k}^{N}\; P_{k}} \right)}{N\;}},{{{wherein}\mspace{14mu} N} = 3},5,{{or}\mspace{14mu} 8\mspace{14mu} \left( {{depending}\mspace{14mu} {on}\mspace{14mu} {pixel}\mspace{14mu} {position}} \right)}} & \left( {52\; a} \right) \\{G_{av} = {{abs}\left( {P - P_{av}} \right)}} & \left( {52\; b} \right)\end{matrix}$

The pixel-to-pixel gradient values (Equation 51) may be used indetermining a dynamic defect case, and the average of the neighboringpixels (Equations 52a and 52b) may be used in identifying speckle cases,as discussed further below.

In one embodiment, dynamic defect detection may be performed by the DPDClogic 932 as follows. First, it is assumed that a pixel is defective ifa certain number of the gradients G_(k) are at or below a particularthreshold, denoted by the variable dynTh (dynamic defect threshold).Thus, for each pixel, a count (C) of the number of gradients forneighboring pixels inside the picture boundaries that are at or belowthe threshold dynTh is accumulated. The threshold dynTh may be acombination of a fixed threshold component and a dynamic thresholdcomponent that may depend on the “activity” present the surroundingpixels. For instance, in one embodiment, the dynamic threshold componentfor dynTh may be determined by calculating a high frequency componentvalue P_(hf) based upon summing the absolute difference between theaverage pixel values P_(av) (Equation 52a) and each neighboring pixel,as illustrated below:

$\begin{matrix}{{P_{hf} = {{\frac{8}{N}{\sum\limits_{k}^{N}\; {{{abs}\left( {P_{av} - P_{k}} \right)}\mspace{14mu} {wherein}\mspace{14mu} N}}} = 3}},5,{{or}\mspace{14mu} 8}} & \left( {52\; c} \right)\end{matrix}$

In instances where the pixel is located at an image corner (N=3) or atan image edge (N=5), the P_(hf) may be multiplied by the 8/3 or 8/5,respectively. As can be appreciated, this ensures that the highfrequency component P_(hf) is normalized based on eight neighboringpixels (N=8).

Once P_(hf) is determined, the dynamic defect detection threshold dynThmay be computed as shown below:

dynTh=dynTh₁+(dynTh₂ ×P _(hf)),  (53)

wherein dynTh₁ represents the fixed threshold component, and whereindynTh₂ represents the dynamic threshold component, and is a multiplierfor P_(hf) in Equation 53. A different fixed threshold component dynTh₁may be provided for each color component, but for each pixel of the samecolor, dynTh₁ is the same. By way of example only, dynTh₁ may be set sothat it is at least above the variance of noise in the image.

The dynamic threshold component dynTh₂ may be determined based on somecharacteristic of the image. For instance, in one embodiment, dynTh₂ maybe determined using stored empirical data regarding exposure and/orsensor integration time. The empirical data may be determined duringcalibration of the image sensor (e.g., 90), and may associate dynamicthreshold component values that may be selected for dynTh₂ with each ofa number of data points. Thus, based upon the current exposure and/orsensor integration time value, which may be determined during statisticsprocessing in the ISP front-end logic 80, dynTh₂ may be determined byselecting the dynamic threshold component value from the storedempirical data that corresponds to the current exposure and/or sensorintegration time value. Additionally, if the current exposure and/orsensor integration time value does not correspond directly to one of theempirical data points, then dynTh₂ may be determined by interpolatingthe dynamic threshold component values associated with the data pointsbetween which the current exposure and/or sensor integration time valuefalls. Further, like the fixed threshold component dynTh₁, the dynamicthreshold component dynTh₂ may have different values for each colorcomponent. Thus, composite threshold value dynTh may vary for each colorcomponent (e.g., R, B, Gr, Gb).

As mentioned above, for each pixel, a count C of the number of gradientsfor neighboring pixels inside the picture boundaries that are at orbelow the threshold dynTh is determined. For instance, for eachneighboring pixel within the raw frame 310, the accumulated count C ofthe gradients G_(k) that are at or below the threshold dynTh may becomputed as follows:

$\begin{matrix}{{C = {\sum\limits_{k}^{N}\left( {G_{k} \leq {dynTh}} \right)}},{{{for}\mspace{14mu} 0} \leq k \leq {7\left( {{only}\mspace{14mu} {for}\mspace{14mu} k\mspace{14mu} {within}\mspace{14mu} {the}\mspace{14mu} {raw}\mspace{14mu} {frame}} \right)}}} & (54)\end{matrix}$

Next, if the accumulated count C is determined to be less than or equalto a maximum count, denoted by the variable dynMaxC, then the pixel maybe considered as a dynamic defect. In one embodiment, different valuesfor dynMaxC may be provided for N=3 (corner), N=5 (edge), and N=8conditions. This logic is expressed below:

if(C≦dynMaxC),then the current pixel P is defective.  (55)

As mentioned above, the location of defective pixels may be stored intothe static defect table. In some embodiments, the minimum gradient value(min(G_(k))) calculated during dynamic defect detection for the currentpixel may be stored and may be used to sort the defective pixels, suchthat a greater minimum gradient value indicates a greater “severity” ofa defect and should be corrected during pixel correction before lesssevere defects are corrected. In one embodiment, a pixel may need to beprocessed over multiple imaging frames before being stored into thestatic defect table, such as by filtering the locations of defectivepixels over time. In the latter embodiment, the location of thedefective pixel may be stored into the static defect table only if thedefect appears in a particular number of consecutive images at the samelocation. Further, in some embodiments, the static defect table may beconfigured to sort the stored defective pixel locations based upon theminimum gradient values. For instance, the highest minimum gradientvalue may indicate a defect of greater “severity.” By ordering thelocations in this manner, the priority of static defect correction maybe set, such that the most severe or important defects are correctedfirst. Additionally, the static defect table may be updated over time toinclude newly detected static defects, and ordering them accordinglybased on their respective minimum gradient values.

Speckle detection, which may occur in parallel with the dynamic defectdetection process described above, may be performed by determining ifthe value G_(av) (Equation 52b) is above a speckle detection thresholdspkTh. Like the dynamic defect threshold dynTh, the speckle thresholdspkTh may also include fixed and dynamic components, referred to byspkTh₁ and spkTh₂, respectively. In general, the fixed and dynamiccomponents spkTh₁ and spkTh₂ may be set more “aggressively” compared tothe dynTh₁ and dynTh₂ values, in order to avoid falsely detectingspeckle in areas of the image that may be more heavily textured andothers, such as text, foliage, certain fabric patterns, etc.Accordingly, in one embodiment, the dynamic speckle threshold componentspkTh₂ may be increased for high-texture areas of the image, anddecreased for “flatter” or more uniform areas. The speckle detectionthreshold spkTh may be computed as shown below:

spkTh=spkTh₁+(spkTh₂ ×P _(hf)),  (56)

wherein spkTh₁ represents the fixed threshold component, and whereinspkTh₂ represents the dynamic threshold component. The detection ofspeckle may then be determined in accordance with the followingexpression:

if(G _(av)>spkTh),then the current pixel P is speckled.  (57)

Once defective pixels have been identified, the DPDC logic 932 may applypixel correction operations depending on the type of defect detected.For instance, if the defective pixel was identified as a static defect,the pixel is replaced with the stored replacement value, as discussedabove (e.g., the value of the previous pixel of the same colorcomponent). If the pixel was identified as either a dynamic defect or asspeckle, then pixel correction may be performed as follows. First,gradients are computed as the sum of the absolute difference between thecenter pixel and a first and second neighbor pixels (e.g., computationof G_(k) of Equation 51) for four directions, a horizontal (h)direction, a vertical (v) direction, a diagonal-positive direction (dp),and a diagonal-negative direction (dn), as shown below:

G _(h) =G ₃ +G ₄  (58)

G _(v) =G ₁ +G ₆  (59)

G _(dp) =G ₂ +G ₅  (60)

G _(dn) =G ₀ +G ₇  (61)

Next, the corrective pixel value P_(c) may be determined via linearinterpolation of the two neighboring pixels associated with thedirectional gradient G_(h), G_(v), G_(dp), and G_(dn) that has thesmallest value. For instance, in one embodiment, the logic statementbelow may express the calculation of P_(C):

$\begin{matrix}{{{if}\left( {\min==G_{h}} \right)}{{P_{c} = \frac{P_{3} + P_{4}}{2}};}{{else}\mspace{14mu} {{if}\left( {\min==G_{v}} \right)}}{{P_{c} = \frac{P_{1} + P_{6}}{2}};}{{else}\mspace{14mu} {{if}\left( {\min==G_{dp}} \right)}}{{P_{c} = \frac{P_{2} + P_{5}}{2}};}{{else}\mspace{14mu} {{if}\left( {\min==G_{dn}} \right)}}{{P_{c} = \frac{P_{0} + P_{7}}{2}};}} & (62)\end{matrix}$

The pixel correction techniques implemented by the DPDC logic 932 mayalso provide for exceptions at boundary conditions. For instance, if oneof the two neighboring pixels associated with the selected interpolationdirection is outside of the raw frame, then the value of the neighborpixel that is within the raw frame is substituted instead. Thus, usingthis technique, the corrective pixel value will be equivalent to thevalue of the neighbor pixel within the raw frame.

It should be noted that the defective pixel detection/correctiontechniques applied by the DPDC logic 932 during the ISP pipe processingis more robust compared to the DPDC logic 738 in the ISP front-end logic80. As discussed in the embodiment above, the DPDC logic 738 performsonly dynamic defect detection and correction using neighboring pixels inonly the horizontal direction, whereas the DPDC logic 932 provides forthe detection and correction of static defects, dynamic defects, as wellas speckle, using neighboring pixels in both horizontal and verticaldirections.

As will be appreciated, the storage of the location of the defectivepixels using a static defect table may provide for temporal filtering ofdefective pixels with lower memory requirements. For instance, comparedto many conventional techniques which store entire images and applytemporal filtering to identify static defects over time, embodiments ofthe present technique only store the locations of defective pixels,which may typically be done using only a fraction of the memory requiredto store an entire image frame. Further, as discussed above, the storingof a minimum gradient value (min(G_(k))), allows for an efficient use ofthe static defect table prioritizing the order of the locations at whichdefective pixels are corrected (e.g., beginning with those that will bemost visible).

Additionally, the use of thresholds that include a dynamic component(e.g., dynTh₂ and spkTh₂) may help to reduce false defect detections, aproblem often encountered in conventional image processing systems whenprocessing high texture areas of an image (e.g., text, foliage, certainfabric patterns, etc.). Further, the use of directional gradients (e.g.,h, v, dp, dn) for pixel correction may reduce the appearance of visualartifacts if a false defect detection occurs. For instance, filtering inthe minimum gradient direction may result in a correction that stillyields acceptable results under most cases, even in cases of falsedetection. Additionally, the inclusion of the current pixel P in thegradient calculation may improve the accuracy of the gradient detection,particularly in the case of hot pixels.

The above-discussed defective pixel detection and correction techniquesimplemented by the DPDC logic 932 may be summarized by a series of flowcharts provided in FIGS. 101-103. For instance, referring first to FIG.101, a process 960 for detecting static defects is illustrated.Beginning initially at step 962, an input pixel P is received at a firsttime, T₀. Next, at step 964, the location of the pixel P is compared tothe values stored in a static defect table. Decision logic 966determines whether the location of the pixel P is found in the staticdefect table. If the location of P is in the static defect table, thenthe process 960 continues to step 968, wherein the pixel P is marked asa static defect and a replacement value is determined. As discussedabove, the replacement value may be determined based upon the value ofthe previous pixel (in scan order) of the same color component. Theprocess 960 then continues to step 970, at which the process 960proceeds to the dynamic and speckle detection process 980, illustratedin FIG. 102. Additionally, if at decision logic 966, the location of thepixel P is determined not to be in the static defect table, then theprocess 960 proceeds to step 970 without performing step 968.

Continuing to FIG. 102, the input pixel P is received at time T1, asshown by step 982, for processing to determine whether a dynamic defector speckle is present. Time T1 may represent a time-shift with respectto the static defect detection process 960 of FIG. 101. As discussedabove, the dynamic defect and speckle detection process may begin afterthe static defect detection process has analyzed two scan lines (e.g.,rows) of pixels, thus allowing time for the identification of staticdefects and their respective replacement values to be determined beforedynamic/speckle detection occurs.

The decision logic 984 determines if the input pixel P was previouslymarked as a static defect (e.g., by step 968 of process 960). If P ismarked as a static defect, then the process 980 may continue to thepixel correction process shown in FIG. 103 and may bypass the rest ofthe steps shown in FIG. 102. If the decision logic 984 determines thatthe input pixel P is not a static defect, then the process continues tostep 986, and neighboring pixels are identified that may be used in thedynamic defect and speckle process. For instance, in accordance with theembodiment discussed above and illustrated in FIG. 100, the neighboringpixels may include the immediate 8 neighbors of the pixel P (e.g.,P0-P7), thus forming a 3×3 pixel area. Next, at step 988, pixel-to-pixelgradients are calculated with respect to each neighboring pixel withinthe raw frame 310, as described in Equation 51 above. Additionally, anaverage gradient (G_(av)) may be calculated as the difference betweenthe current pixel and the average of its surrounding pixels, as shown inEquations 52a and 52b.

The process 980 then branches to step 990 for dynamic defect detectionand to decision logic 998 for speckle detection. As noted above, dynamicdefect detection and speckle detection may, in some embodiments, occurin parallel. At step 990, a count C of the number of gradients that areless than or equal to the threshold dynTh is determined. As describedabove, the threshold dynTh may include fixed and dynamic components and,in one embodiment, may be determined in accordance with Equation 53above. If C is less than or equal to a maximum count, dynMaxC, then theprocess 980 continues to step 996, and the current pixel is marked asbeing a dynamic defect. Thereafter, the process 980 may continue to thepixel correction process shown in FIG. 103, which will be discussedbelow.

Returning back the branch after step 988, for speckle detection, thedecision logic 998 determines whether the average gradient G_(av) isgreater than a speckle detection threshold spkTh, which may also includea fixed and dynamic component. If G_(av) is greater than the thresholdspkTh, then the pixel P is marked as containing speckle at step 1000and, thereafter, the process 980 continues to FIG. 103 for thecorrection of the speckled pixel. Further, if the output of both of thedecision logic blocks 992 and 998 are “NO,” then this indicates that thepixel P does not contain dynamic defects, speckle, or even staticdefects (decision logic 984). Thus, when the outputs of decision logic992 and 998 are both “NO,” the process 980 may conclude at step 994,whereby the pixel P is passed unchanged, as no defects (e.g., static,dynamic, or speckle) were detected.

Continuing to FIG. 103, a pixel correction process 1010 in accordancewith the techniques described above is provided. At step 1012, the inputpixel P is received from process 980 of FIG. 102. It should be notedthat the pixel P may be received by process 1010 from step 984 (staticdefect) or from steps 996 (dynamic defect) and 1000 (speckle defect).The decision logic 1014 then determines whether the pixel P is marked asa static defect. If the pixel P is a static defect, then the process1010 continues and ends at step 1016, whereby the static defect iscorrected using the replacement value determined at step 968 (FIG. 101).

If the pixel P is not identified as a static defect, then the process1010 continues from decision logic 1014 to step 1018, and directionalgradients are calculated. For instance, as discussed above withreference to Equations 58-61, the gradients may be computed as the sumof the absolute difference between the center pixel and first and secondneighboring pixels for four directions (h, v, dp, and dn). Next, at step1020, the directional gradient having the smallest value is identifiedand, thereafter, decision logic 1022 assesses whether one of the twoneighboring pixels associated with the minimum gradient is locatedoutside of the image frame (e.g., raw frame 310). If both neighboringpixels are within the image frame, then the process 1010 continues tostep 1024, and a pixel correction value (P_(C)) is determined byapplying linear interpolation to the values of the two neighboringpixels, as illustrated by Equation 62. Thereafter, the input pixel P maybe corrected using the interpolated pixel correction value P_(C), asshown at step 1030.

Returning to the decision logic 1022, if it is determined that one ofthe two neighboring pixels are located outside of the image frame (e.g.,raw frame 165), then instead of using the value of the outside pixel(Pout), the DPDC logic 932 may substitute the value of Pout with thevalue of the other neighboring pixel that is inside the image frame(Pin), as shown at step 1026. Thereafter, at step 1028, the pixelcorrection value P_(C) is determined by interpolating the values of Pinand the substituted value of Pout. In other words, in this case, P_(C)may be equivalent to the value of Pin. Concluding at step 1030, thepixel P is corrected using the value P_(C). Before continuing, it shouldbe understood that the particular defective pixel detection andcorrection processes discussed herein with reference to the DPDC logic932 are intended to reflect only one possible embodiment of the presenttechnique. Indeed, depending on design and/or cost constraints, a numberof variations are possible, and features may be added or removed suchthat the overall complexity and robustness of the defectdetection/correction logic is between the simpler detection/correctionlogic 738 implemented in the ISP front-end block 80 and the defectdetection/correction logic discussed here with reference to the DPDClogic 932.

Referring back to FIG. 99, the corrected pixel data is output from theDPDC logic 932 and then received by the noise reduction logic 934 forfurther processing. In one embodiment, the noise reduction logic 934 maybe configured to implements two-dimensional edge-adaptive low passfiltering to reduce noise in the image data while maintaining detailsand textures. The edge-adaptive thresholds may be set (e.g., by thecontrol logic 84) based upon the present lighting levels, such thatfiltering may be strengthened under low light conditions. Further, asbriefly mentioned above with regard to the determination of the dynThand spkTh values, noise variance may be determined ahead of time for agiven sensor so that the noise reduction thresholds can be set justabove noise variance, such that during the noise reduction processing,noise is reduced without significantly affecting textures and details ofthe scene (e.g., avoid/reduce false detections). Assuming a Bayer colorfilter implementation, the noise reduction logic 934 may process eachcolor component Gr, R, B, and Gb independently using a separable 7-taphorizontal filter and a 5-tap vertical filter. In one embodiment, thenoise reduction process may be carried out by correcting fornon-uniformity on the green color components (Gb and Gr), and thenperforming horizontal filtering and vertical filtering.

Green non-uniformity (GNU) is generally characterized by a slightbrightness difference between the Gr and Gb pixels given a uniformlyilluminated flat surface. Without correcting or compensating for thisnon-uniformity, certain artifacts, such as a “maze” artifact, may appearin the full color image after demosaicing. During the greennon-uniformity process may include determining, for each green pixel inthe raw Bayer image data, if the absolute difference between a currentgreen pixel (G1) and the green pixel to the right and below (G2) thecurrent pixel is less than a GNU correction threshold (gnuTh). FIG. 104illustrates the location of the G1 and G2 pixels in a 2×2 area of theBayer pattern. As shown, the color of the pixels bordering G1 may bedepending upon whether the current green pixel is a Gb or Gr pixel. Forinstance, if G1 is Gr, then G2 is Gb, the pixel to the right of G1 is R(red), and the pixel below G1 is B (blue). Alternatively, if G1 is Gb,then G2 is Gr, and the pixel to the right of G1 is B, whereas the pixelbelow G1 is R. If the absolute difference between G1 and G2 is less thanthe GNU correction threshold value, then current green pixel G1 isreplaced by the average of G1 and G2, as shown by the logic below:

$\begin{matrix}{{{if}\left( {{{abs}\left( {G\; 1\text{-}G\; 2} \right)} \leq {gnuTh}} \right)};{{G\; 1} = \frac{{G\; 1} + {G\; 2}}{2}}} & (63)\end{matrix}$

As can be appreciated, the application of green non-uniformitycorrection in this manner may help to prevent the G1 and G2 pixels frombeing averaged across edges, thus improving and/or preserving sharpness.

Horizontal filtering is applied subsequent to green non-uniformitycorrection and may, in one embodiment, provide a 7-tap horizontalfilter. Gradients across the edge of each filter tap are computed, andif it is above a horizontal edge threshold (horzTh), the filter tap isfolded to the center pixel, as will be illustrated below. In certainembodiments, the noise filtering may be edge adaptive. For instance, thehorizontal filter may be a finite impulse response (FIR) filter wherethe filter taps are used only if the difference between the center pixeland the pixel at the tap is smaller then a threshold that depends onnoise variance. The horizontal filter may process the image dataindependently for each color component (R, B, Gr, Gb) and may useunfiltered values as inputs values.

By way of example, FIG. 105 shows a graphical depiction of a set ofhorizontal pixels P0 to P6, with a center tap positioned at P3. Basedupon the pixels shown in FIG. 105, edge gradients for each filter tapmay be calculated as follows:

Eh0=abs(P0−P1)  (64)

Eh1=abs(P1−P2)  (65)

Eh2=abs(P2−P3)  (66)

Eh3=abs(P3−P4)  (67)

Eh4=abs(P4−P5)  (68)

Eh5=abs(P5−P6)  (69)

The edge gradients Eh0-Eh5 may then be utilized by the horizontal filtercomponent to determine a horizontal filtering output, P_(horz), usingthe formula shown in Equation 70 below:

$\begin{matrix}{{P_{horz} = {{C\; 0 \times \left\lbrack {{{\left( {{{Eh}\; 2} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{{{\left( {{{Eh}\; 1} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 2}:{{{\left( {{{Eh}\; 0} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 1}:{P\; 0}}}} \right\rbrack} + {C\; 1 \times \left\lbrack {{{\left( {{{Eh}\; 2} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{{{\left( {{{Eh}\; 1} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 2}:{P\; 1}}} \right\rbrack} + {C\; 2 \times \left\lbrack {{{\left( {{{Eh}\; 2} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{P\; 2}} \right\rbrack} + {C\; 3 \times P\; 3} + {C\; 4 \times \left\lbrack {{{\left( {{{Eh}\; 3} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{P\; 4}} \right\rbrack} + {C\; 5 \times \left\lbrack {{{\left( {{{Eh}\; 3} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{{{\left( {{{Eh}\; 4} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 4}:{P\; 5}}} \right\rbrack} + {C\; 6 \times \left\lbrack {{{\left( {{{Eh}\; 3} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 3}:{{{\left( {{{Eh}\; 4} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 4}:{{{\left( {{{Eh}\; 5} > {{horzTh}\lbrack c\rbrack}} \right)?P}\; 5}:{P\; 6}}}} \right\rbrack}}},} & (70)\end{matrix}$

wherein horzTh[c] is the horizontal edge threshold for each colorcomponent c (e.g., R, B, Gr, and Gb), and wherein C0-C6 are the filtertap coefficients corresponding to pixels P0-P6, respectively. Thehorizontal filter output P_(horz) may be applied at the center pixel P3location. In one embodiment, the filter tap coefficients C0-C6 may be16-bit two's complement values with 3 integer bits and 13 fractionalbits (3.13 in floating point). Further, it should be noted that thefilter tap coefficients C0-C6 need not necessarily be symmetrical withrespect to the center pixel P3.

Vertical filtering is also applied by the noise reduction logic 934subsequent to green non-uniformity correction and horizontal filteringprocesses. In one embodiment, the vertical filter operation may providea 5-tap filter, as shown in FIG. 106, with the center tap of thevertical filter located at P2. The vertical filtering process may occurin a similar manner as the horizontal filtering process described above.For instance, gradients across the edge of each filter tap are computed,and if it is above a vertical edge threshold (vertTh), the filter tap isfolded to the center pixel P2. The vertical filter may process the imagedata independently for each color component (R, B, Gr, Gb) and may useunfiltered values as inputs values.

Based upon the pixels shown in FIG. 106, vertical edge gradients foreach filter tap may be calculated as follows:

Ev0=abs(P0−P1)  (71)

Ev1=abs(P1−P2)  (72)

Ev2=abs(P2−P3)  (73)

Ev3=abs(P3−P4)  (74)

The edge gradients Ev0-Ev5 may then be utilized by the vertical filterto determine a vertical filtering output, P_(vert), using the formulashown in Equation 75 below:

$\begin{matrix}{{P_{vert} = {{C\; 0 \times \left\lbrack {{{\left( {{{Ev}\; 1} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 2}:{{{\left( {{{Ev}\; 0} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 1}:{P\; 0}}} \right\rbrack} + {C\; 1 \times \left\lbrack {{{\left( {{{Ev}\; 1} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 2}:{P\; 1}} \right\rbrack} + {C\; 2 \times P\; 2} + {C\; 3 \times \left\lbrack {{{\left( {{{Ev}\; 2} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 2}:{P\; 3}} \right\rbrack} + {C\; 4 \times \left\lbrack {{{\left( {{{Ev}\; 2} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 2}:{{{\left( {{{Eh}\; 3} > {{vertTh}\lbrack c\rbrack}} \right)?P}\; 3}:{P\; 4}}} \right\rbrack}}},} & (75)\end{matrix}$

wherein vertTh[c] is the vertical edge threshold for each colorcomponent c (e.g., R, B, Gr, and Gb), and wherein C0-C4 are the filtertap coefficients corresponding to the pixels P0-P4 of FIG. 106,respectively. The vertical filter output P_(vert) may be applied at thecenter pixel P2 location. In one embodiment, the filter tap coefficientsC0-C4 may be 16-bit two's complement values with 3 integer bits and 13fractional bits (3.13 in floating point). Further, it should be notedthat the filter tap coefficients C0-C4 need not necessarily besymmetrical with respect to the center pixel P2.

Additionally, with regard to boundary conditions, when neighboringpixels are outside of the raw frame 310 (FIG. 23), the values of theout-of-bound pixels are replicated with the value of same color pixel atthe edge of the raw frame. This convention may be implemented for bothhorizontal and vertical filtering operations. By way of example,referring again to FIG. 105, in the case of horizontal filtering, if thepixel P2 is an edge pixel at the left-most edge of the raw frame, andthe pixels P0 and P1 are outside of the raw frame, then the values ofthe pixels P0 and P1 are substituted with the value of the pixel P2 forhorizontal filtering.

Referring again back to the block diagram of the raw processing logic900 shown in FIG. 99, the output of the noise reduction logic 934 issubsequently sent to the lens shading correction (LSC) logic 936 forprocessing. As discussed above, lens shading correction techniques mayinclude applying an appropriate gain on a per-pixel basis to compensatefor drop-offs in light intensity, which may be the result of thegeometric optics of the lens, imperfections in manufacturing,misalignment of the microlens array and the color array filter, and soforth. Further, the infrared (IR) filter in some lenses may cause thedrop-off to be illuminant-dependent and, thus, lens shading gains may beadapted depending upon the light source detected.

In the depicted embodiment, the LSC logic 936 of the ISP pipe 82 may beimplemented in a similar manner, and thus provide generally the samefunctions, as the LSC logic 740 of the ISP front-end block 80, asdiscussed above with reference to FIGS. 71-79. Accordingly, in order toavoid redundancy, it should be understood that the LSC logic 936 of thepresently illustrated embodiment is configured to operate in generallythe same manner as the LSC logic 740 and, as such, the description ofthe lens shading correction techniques provided above will not berepeated here. However, to generally summarize, it should be understoodthat the LSC logic 936 may process each color component of the raw pixeldata stream independently to determine a gain to apply to the currentpixel. In accordance with the above-discussed embodiments, the lensshading correction gain may be determined based upon a defined set ofgain grid points distributed across the imaging frame, wherein theinterval between each grid point is defined by a number of pixels (e.g.,8 pixels, 16 pixels etc.). If the location of the current pixelcorresponds to a grid point, then the gain value associated with thatgrid point is applied to the current pixel. However, if the location ofthe current pixel is between grid points (e.g., G0, G1, G2, and G3 ofFIG. 74), then the LSC gain value may be calculated by interpolation ofthe grid points between which the current pixel is located (Equations13a and 13b). This process is depicted by the process 772 of FIG. 75.Further, as mentioned above with respect to FIG. 73, in someembodiments, the grid points may be distributed unevenly (e.g.,logarithmically), such that the grid points are less concentrated in thecenter of the LSC region 760, but more concentrated towards the cornersof the LSC region 760, typically where lens shading distortion is morenoticeable.

Additionally, as discussed above with reference to FIGS. 78 and 79, theLSC logic 936 may also apply a radial gain component with the grid gainvalues. The radial gain component may be determined based upon distanceof the current pixel from the center of the image (Equations 14-16). Asmentioned, using a radial gain allows for the use of single common gaingrid for all color components, which may greatly reduce the totalstorage space required for storing separate gain grids for each colorcomponent. This reduction in grid gain data may decrease implementationcosts, as grid gain data tables may account for a significant portion ofmemory or chip area in image processing hardware.

Next, referring again to the raw processing logic block diagram 900 ofFIG. 99, the output of the LSC logic 936 is then passed to a secondgain, offset, and clamping (GOC) block 938. The GOC logic 938 may beapplied prior to demosaicing (by logic block 940) and may be used toperform auto-white balance on the output of the LSC logic 936. In thedepicted embodiment, the GOC logic 938 may be implemented in the samemanner as the GOC logic 930 (and the BLC logic 739). Thus, in accordancewith the Equation 11 above, the input received by the GOC logic 938 isfirst offset by a signed value and then multiplied by a gain. Theresulting value is then clipped to a minimum and a maximum range inaccordance with Equation 12.

Thereafter, the output of the GOC logic 938 is forwarded to thedemosaicing logic 940 for processing to produce a full color (RGB) imagebased upon the raw Bayer input data. As will be appreciated, the rawoutput of an image sensor using a color filter array, such as a Bayerfilter is “incomplete” in the sense that each pixel is filtered toacquire only a single color component. Thus, the data collected for anindividual pixel alone is insufficient to determine color. Accordingly,demosaicing techniques may be used to generate a full color image fromthe raw Bayer data by interpolating the missing color data for eachpixel.

Referring now to FIG. 107, a graphical process flow 692 that provides ageneral overview as to how demosaicing may be applied to a raw Bayerimage pattern 1034 to produce a full color RGB is illustrated. As shown,a 4×4 portion 1036 of the raw Bayer image 1034 may include separatechannels for each color component, including a green channel 1038, a redchannel 1040, and a blue channel 1042. Because each imaging pixel in aBayer sensor only acquires data for one color, the color data for eachcolor channel 1038, 1040, and 1042 may be incomplete, as indicated bythe “?” symbols. By applying a demosaicing technique 1044, the missingcolor samples from each channel may be interpolated. For instance, asshown by reference number 1046, interpolated data G′ may be used to fillthe missing samples on the green color channel Similarly, interpolateddata R′ may (in combination with the interpolated data G′ 1046) be usedto fill the missing samples on the red color channel 1048, andinterpolated data B′ may (in combination with the interpolated data G′1046) be used to fill the missing samples on the blue color channel1050. Thus, as a result of the demosaicing process, each color channel(R, G, B) will have a full set of color data, which may then be used toreconstruct a full color RGB image 1052.

A demosaicing technique that may be implemented by the demosaicing logic940 will now be described in accordance with one embodiment. On thegreen color channel, missing color samples may be interpolated using alow pass directional filter on known green samples and a high pass (orgradient) filter on the adjacent color channels (e.g., red and blue).For the red and blue color channels, the missing color samples may beinterpolated in a similar manner, but by using low pass filtering onknown red or blue values and high pass filtering on co-locatedinterpolated green values. Further, in one embodiment, demosaicing onthe green color channel may utilize a 5×5 pixel block edge-adaptivefilter based on the original Bayer color data. As will be discussedfurther below, the use of an edge-adaptive filter may provide for thecontinuous weighting based on gradients of horizontal and verticalfiltered values, which reduce the appearance of certain artifacts, suchas aliasing, “checkerboard,” or “rainbow” artifacts, commonly seen inconventional demosaicing techniques.

During demosaicing on the green channel, the original values for thegreen pixels (Gr and Gb pixels) of the Bayer image pattern are used.However, in order to obtain a full set of data for the green channel,green pixel values may be interpolated at the red and blue pixels of theBayer image pattern. In accordance with the present technique,horizontal and vertical energy components, respectively referred to asEh and Ev, are first calculated at red and blue pixels based on theabove-mentioned5×5 pixel block. The values of Eh and Ev may be used toobtain an edge-weighted filtered value from the horizontal and verticalfiltering steps, as discussed further below.

By way of example, FIG. 108 illustrates the computation of the Eh and Evvalues for a red pixel centered in the 5×5 pixel block at location (j,i), wherein j corresponds to a row and i corresponds to a column. Asshown, the calculation of Eh considers the middle three rows (j−1, j,j+1) of the 5×5 pixel block, and the calculation of Ev considers themiddle three columns (i−1, i, i+1) of the 5×5 pixel block. To computeEh, the absolute value of the sum of each of the pixels in the redcolumns (i−2, i, i+2) multiplied by a corresponding coefficient (e.g.,−1 for columns i−2 and i+2; 2 for column i) is summed with the absolutevalue of the sum of each of the pixels in the blue columns (i−1, i+1)multiplied by a corresponding coefficient (e.g., 1 for column i−1; −1for column i+1). To compute Ev, the absolute value of the sum of each ofthe pixels in the red rows (j−2, j, j+2) multiplied by a correspondingcoefficient (e.g., −1 for rows j−2 and j+2; 2 for row j) is summed withthe absolute value of the sum of each of the pixels in the blue rows(j−1, j+1) multiplied by a corresponding coefficient (e.g., 1 for rowj−1; −1 for row j+1). These computations are illustrated by Equations 76and 77 below:

$\begin{matrix}{{Eh} = {{abs}\left\lbrack {2\left( {\left( {{P\left( {{j - 1},i} \right)} + {P\left( {j,i} \right)} + {P\left( {{j + 1},i} \right)}} \right) - \left( {{P\left( {{j - 1},{i - 2}} \right)} + {P\left( {j,{i - 2}} \right)} + {P\left( {{j + 1},{i - 2}} \right)}} \right) - \left( {{P\left( {{j - 1},{i + 2}} \right)} + {P\left( {j,{i + 2}} \right)} + {P\left( {{j + 1},{i + 2}} \right)}} \right\rbrack + {{abs}\left\lbrack {\left( {{P\left( {{j - 1},{i - 1}} \right)} + {P\left( {j,{i - 1}} \right)} + {P\left( {{j + 1},{i - 1}} \right)}} \right) - \left( {{P\left( {{j - 1},{i + 1}} \right)} + {P\left( {j,{i + 1}} \right)} + {P\left( {{j + 1},{i + 1}} \right)}} \right\rbrack} \right.}} \right.} \right.}} & (76) \\{{Ev} = {{abs}\left\lbrack {{2\left( {{P\left( {j,{i - 1}} \right)} + {P\left( {j,i} \right)} + {P\left( {j,{i + 1}} \right)}} \right)} - \left( {{P\left( {{j - 2},{i - 1}} \right)} + {P\left( {{j - 2},i} \right)} + {P\left( {{j - 2},{i + 1}} \right)}} \right) - \left( {{P\left( {{j + 2},{i - 1}} \right)} + {P\left( {{j + 2},i} \right)} + {P\left( {{j + 2},{i + 1}} \right\rbrack} + {{abs}\left\lbrack {\left( {{P\left( {{j - 1},{i - 1}} \right)} + {P\left( {{j - 1},i} \right)} + {P\left( {{j - 1},{i + 1}} \right)}} \right) - \left( {{P\left( {{j + 1},{i - 1}} \right)} + {P\left( {{j + 1},i} \right)} + {P\left( {{j + 1},{i + 1}} \right)}} \right\rbrack} \right.}} \right.} \right.}} & (77)\end{matrix}$

Thus, the total energy sum may be expressed as: Eh+Ev. Further, whilethe example shown in FIG. 108 illustrates the computation of Eh and Evfor a red center pixel at (j, i), it should be understood that the Ehand Ev values may be determined in a similar manner for blue centerpixels.

Next, horizontal and vertical filtering may be applied to the Bayerpattern to obtain the vertical and horizontal filtered values Gh and Gv,which may represent interpolated green values in the horizontal andvertical directions, respectively. The filtered values Gh and Gv may bedetermined using a low pass filter on known neighboring green samples inaddition to using directional gradients of the adjacent color (R or B)to obtain a high frequency signal at the locations of the missing greensamples. For instance, with reference to FIG. 109, an example ofhorizontal interpolation for determining Gh will now be illustrated.

As shown in FIG. 109, five horizontal pixels (R0, G1, R2, G3, and R4) ofa red line 1060 of the Bayer image, wherein R2 is assumed to be thecenter pixel at (j, i), may be considered in determining Gh. Filteringcoefficients associated with each of these five pixels are indicated byreference numeral 1062. Accordingly, the interpolation of a green value,referred to as G2′, for the center pixel R2, may be determined asfollows:

$\begin{matrix}{{G\; 2^{\prime}} = {\frac{{G\; 1} + {G\; 3}}{2} + \frac{{2\; R\; 2} - \left( \frac{{R\; 0} + {R2}}{2} \right) - \left( \frac{{R\; 2} + {R\; 4}}{2} \right)}{2}}} & (78)\end{matrix}$

Various mathematical operations may then be utilized to produce theexpression for G2′ shown in Equations 79 and 80 below:

$\begin{matrix}{{G\; 2^{\prime}} = {\frac{{2\; G\; 1} + {2\; G\; 3}}{4} + \frac{{4\; R\; 2} - {R\; 0} - {R\; 2} - {R\; 2} - {R\; 4}}{4}}} & (79) \\{{G\; 2^{\prime}} = \frac{{2\; G\; 1} + {2\; G\; 3} + {2\; R\; 2} - {R\; 0} - {R\; 4}}{4}} & (80)\end{matrix}$

Thus, with reference to FIG. 109 and the Equations 78-80 above, thegeneral expression for the horizontal interpolation for the green valueat (j, i) may be derived as:

$\begin{matrix}{{G\; h} = \frac{\begin{pmatrix}{{2\; {P\left( {j,{i - 1}} \right)}} + {2\; {P\left( {j,{i + 1}} \right)}} + {2\; P\left( {j,i} \right)} -} \\{{P\left( {j,{i - 2}} \right)} - {P\left( {j,{i + 2}} \right)}}\end{pmatrix}}{4}} & (81)\end{matrix}$

The vertical filtering component Gv may be determined in a similarmanner as Gh. For example, referring to FIG. 110, five vertical pixels(R0, G1, R2, G3, and R4) of a red column 1064 of the Bayer image andtheir respective filtering coefficients 1068, wherein R2 is assumed tobe the center pixel at (j, i), may be considered in determining Gv.Using low pass filtering on the known green samples and high passfiltering on the red channel in the vertical direction, the followingexpression may be derived for Gv:

$\begin{matrix}{{Gv} = \frac{\begin{pmatrix}{{2\; {P\left( {{j - 1},i} \right)}} + {2\; P\left( {{j + 1},i} \right)} +} \\{{2\; {P\left( {j,i} \right)}} - {P\left( {{j - 2},i} \right)} - {P\left( {{j + 2},i} \right)}}\end{pmatrix}}{4}} & (82)\end{matrix}$

While the examples discussed herein have shown the interpolation ofgreen values on a red pixel, it should be understood that theexpressions set forth in Equations 81 and 82 may also be used in thehorizontal and vertical interpolation of green values for blue pixels.

The final interpolated green value G′ for the center pixel (j, i) may bedetermined by weighting the horizontal and vertical filter outputs (Ghand Gv) by the energy components (Eh and Ev) discussed above to yieldthe following equation:

$\begin{matrix}{{G^{\prime}\left( {j,i} \right)} = {{\left( \frac{Ev}{{Eh} + {Ev}} \right){Gh}} + {\left( \frac{Eh}{{Eh} + {Ev}} \right){Gv}}}} & (83)\end{matrix}$

As discussed above, the energy components Eh and Ev may provide foredge-adaptive weighting of the horizontal and vertical filter outputs Ghand Gv, which may help to reduce image artifacts, such as rainbow,aliasing, or checkerboard artifacts, in the reconstructed RGB image.Additionally, the demosaicing logic 940 may provide an option to bypassthe edge-adaptive weighting feature by setting the Eh and Ev values eachto 1, such that Gh and Gv are equally weighted.

In one embodiment, the horizontal and vertical weighting coefficients,shown in Equation 51 above, may be quantized to reduce the precision ofthe weighting coefficients to a set of “coarse” values. For instance, inone embodiment, the weighting coefficients may be quantized to eightpossible weight ratios: 1/8, 2/8, 3/8, 4/8, 5/8, 6/8, 7/8, and 8/8.Other embodiments may quantize the weighting coefficients into 16 values(e.g., 1/16 to 16/16), 32 values (1/32 to 32/32), and so forth. As canbe appreciated, when compared to using full precision values (e.g.,32-bit floating point values), the quantization of the weightcoefficients may reduce the implementation complexity when determiningand applying the weighting coefficients to horizontal and verticalfilter outputs.

In further embodiments, the presently disclosed techniques, in additionto determining and using horizontal and vertical energy components toapply weighting coefficients to the horizontal (Gh) and vertical (Gv)filtered values, may also determine and utilize energy components in thediagonal-positive and diagonal-negative directions. For instance, insuch embodiments, filtering may also be applied in the diagonal-positiveand diagonal-negative directions. Weighting of the filter outputs mayinclude selecting the two highest energy components, and using theselected energy components to weight their respective filter outputs.For example, assuming that the two highest energy components correspondto the vertical and diagonal-positive directions, the vertical anddiagonal-positive energy components are used to weight the vertical anddiagonal-positive filter outputs to determine the interpolated greenvalue (e.g., at a red or blue pixel location in the Bayer pattern).

Next, demosaicing on the red and blue color channels may be performed byinterpolating red and blue values at the green pixels of the Bayer imagepattern, interpolating red values at the blue pixels of the Bayer imagepattern, and interpolating blue values at the red pixels of the Bayerimage pattern. In accordance with the present discussed techniques,missing red and blue pixel values may be interpolated using low passfiltering based upon known neighboring red and blue pixels and high passfiltering based upon co-located green pixel values, which may beoriginal or interpolated values (from the green channel demosaicingprocess discussed above) depending on the location of the current pixel.Thus, with regard to such embodiments, it should be understood thatinterpolation of missing green values may be performed first, such thata complete set of green values (both original and interpolated values)is available when interpolating the missing red and blue samples.

The interpolation of red and blue pixel values may be described withreference to FIG. 111, which illustrates various 3×3 blocks of the Bayerimage pattern to which red and blue demosaicing may be applied, as wellas interpolated green values (designated by G′) that may have beenobtained during demosaicing on the green channel. Referring first toblock 1070, the interpolated red value, R′₁₁, for the Gr pixel (G₁₁) maybe determined as follows:

$\begin{matrix}{{R_{11}^{\prime} = {\frac{\left( {R_{10} + R_{12}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{10}^{\prime} - G_{12}^{\prime}} \right)}{2}}},} & (84)\end{matrix}$

where G′₁₀ and G′₁₂ represent interpolated green values, as shown byreference number 1078. Similarly, the interpolated blue value, B′₁₁, forthe Gr pixel (G₁₁) may be determined as follows:

$\begin{matrix}{{B_{11}^{\prime} = {\frac{\left( {B_{01} + B_{21}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{01}^{\prime} - G_{21}^{\prime}} \right)}{2}}},} & (85)\end{matrix}$

wherein G′₀₁ and G′₂₁ represent interpolated green values (1078).

Next, referring to the pixel block 1072, in which the center pixel is aGb pixel (G₁₁), the interpolated red value, R′₁₁, and blue value B′₁₁,may be determined as shown in Equations 86 and 87 below:

$\begin{matrix}{R_{11}^{\prime} = {\frac{\left( {R_{01} + R_{21}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{01}^{\prime} - G_{21}^{\prime}} \right)}{2}}} & (86) \\{B_{11}^{\prime} = {\frac{\left( {B_{10} + B_{12}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{10}^{\prime} - G_{12}^{\prime}} \right)}{2}}} & (87)\end{matrix}$

Further, referring to pixel block 1074, the interpolation of a red valueon a blue pixel, B₁₁, may be determined as follows:

$\begin{matrix}{{R_{11}^{\prime} = {\frac{\left( {R_{00} + R_{02} + R_{20} + R_{22}} \right)}{4} + \frac{\left( {{4\; G_{11}^{\prime}} - G_{00}^{\prime} - G_{02}^{\prime} - G_{20}^{\prime} - G_{22}^{\prime}} \right)}{4}}},} & (88)\end{matrix}$

wherein G′₀₀, G′₀₂, G′₁₁, G′₂₀, and G′₂₂ represent interpolated greenvalues, as shown by reference number 1080. Finally, the interpolation ofa blue value on a red pixel, as shown by pixel block 1076, may becalculated as follows:

$\begin{matrix}{B_{11}^{\prime} = {\frac{\left( {B_{00} + B_{02} + B_{20} + B_{22}} \right)}{4} + \frac{\left( {{4\; G_{11}^{\prime}} - G_{00}^{\prime} - G_{02}^{\prime} - G_{20}^{\prime} - G_{22}^{\prime}} \right)}{4}}} & (89)\end{matrix}$

While the embodiment discussed above relied on color differences (e.g.,gradients) for determining red and blue interpolated values, anotherembodiment may provide for interpolated red and blue values using colorratios. For instance, interpolated green values (blocks 1078 and 1080)may be used to obtain a color ratio at red and blue pixel locations ofthe Bayer image pattern, and linear interpolation of the ratios may beused to determine an interpolated color ratio for the missing colorsample. The green value, which may be an interpolated or an originalvalue, may be multiplied by the interpolated color ratio to obtain afinal interpolated color value. For instance, interpolation of red andblue pixel values using color ratios may be performed in accordance withthe formulas below, wherein Equations 90 and 91 show the interpolationof red and blue values for a Gr pixel, Equations 92 and 93 show theinterpolation of red and blue values for a Gb pixel, Equation 94 showsthe interpolation of a red value on a blue pixel, and Equation 95 showsthe interpolation of a blue value on a red pixel:

$\begin{matrix}{{R_{11}^{\prime} = {G_{11}\frac{\left( \frac{R_{10}}{G_{10}^{\prime}} \right) + \left( \frac{R_{12}}{G_{12}^{\prime}} \right)}{2}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gr}\mspace{14mu} {pixel}} \right)} & (90) \\{{B_{11}^{\prime} = {G_{11}\frac{\left( \frac{B_{01}}{G_{01}^{\prime}} \right) + \left( \frac{B_{21}}{G_{21}^{\prime}} \right)}{2}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gr}\mspace{14mu} {pixel}} \right)} & (91) \\{{R_{11}^{\prime} = {G_{11}\frac{\left( \frac{R_{01}}{G_{01}^{\prime}} \right) + \left( \frac{R_{21}}{G_{21}^{\prime}} \right)}{2}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gb}\mspace{14mu} {pixel}} \right)} & (92) \\{{B_{11}^{\prime} = {G_{11}\frac{\left( \frac{B_{10}}{G_{10}^{\prime}} \right) + \left( \frac{B_{12}}{G_{12}^{\prime}} \right)}{2}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gb}\mspace{14mu} {pixel}} \right)} & (93) \\{{R_{11}^{\prime} = {G_{11}^{\prime}\frac{\left( \frac{R_{00}}{G_{00}^{\prime}} \right) + \left( \frac{R_{02}}{G_{02}^{\prime}} \right) + \left( \frac{R_{20}}{G_{20}^{\prime}} \right) + \left( \frac{R_{22}}{G_{22}^{\prime}} \right)}{4}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {on}\mspace{14mu} a\mspace{14mu} {blue}\mspace{14mu} {pixel}\mspace{14mu} B_{11}} \right)} & (94) \\{{B_{11}^{\prime} = {G_{11}^{\prime}\frac{\left( \frac{B_{00}}{G_{00}^{\prime}} \right) + \left( \frac{B_{02}}{G_{02}^{\prime}} \right) + \left( \frac{B_{20}}{G_{20}^{\prime}} \right) + \left( \frac{B_{22}}{G_{22}^{\prime}} \right)}{4}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {on}\mspace{14mu} a\mspace{14mu} {red}\mspace{14mu} {pixel}\mspace{14mu} R_{11}} \right)} & (95)\end{matrix}$

Once the missing color samples have been interpolated for each imagepixel from the Bayer image pattern, a complete sample of color valuesfor each of the red, blue, and green color channels (e.g., 1046, 1048,and 1050 of FIG. 107) may be combined to produce a full color RGB image.For instance, referring back FIGS. 98 and 99, the output 910 of the rawpixel processing logic 900 may be an RGB image signal in 8, 10, 12 or14-bit formats.

Referring now to FIGS. 112-115, various flow charts illustratingprocesses for demosaicing a raw Bayer image pattern in accordance withdisclosed embodiments are illustrated. Specifically, the process 1082 ofFIG. 112 depicts the determination of which color components are to beinterpolated for a given input pixel P. Based on the determination byprocess 1082, one or more of the process 1100 (FIG. 113) forinterpolating a green value, the process 1112 (FIG. 114) forinterpolating a red value, or the process 1124 (FIG. 115) forinterpolating a blue value may be performed (e.g., by the demosaicinglogic 940).

Beginning with FIG. 112, the process 1082 begins at step 1084 when aninput pixel P is received. Decision logic 1086 determines the color ofthe input pixel. For instance, this may depend on the location of thepixel within the Bayer image pattern. Accordingly, if P is identified asbeing a green pixel (e.g., Gr or Gb), the process 1082 proceeds to step1088 to obtain interpolated red and blue values for P. This may include,for example, continuing to the processes 1112 and 1124 of FIGS. 114 and115, respectively. If P is identified as being a red pixel, then theprocess 1082 proceeds to step 1090 to obtain interpolated green and bluevalues for P. This may include further performing the processes 1100 and1124 of FIGS. 113 and 115, respectively. Additionally, if P isidentified as being a blue pixel, then the process 1082 proceeds to step1092 to obtain interpolated green and red values for P. This may includefurther performing the processes 1100 and 1112 of FIGS. 113 and 114,respectively. Each of the processes 1100, 1112, and 1124 are describedfurther below.

The process 1100 for determining an interpolated green value for theinput pixel P is illustrated in FIG. 113 and includes steps 1102-1110.At step 1102, the input pixel P is received (e.g., from process 1082).Next, at step 1104, a set of neighboring pixels forming a 5×5 pixelblock is identified, with P being the center of the 5×5 block.Thereafter, the pixel block is analyzed to determine horizontal andvertical energy components at step 1106. For instance, the horizontaland vertical energy components may be determined in accordance withEquations 76 and 77 for calculating Eh and Ev, respectively. Asdiscussed, the energy components Eh and Ev may be used as weightingcoefficients to provide edge-adaptive filtering and, therefore, reducethe appearance of certain demosaicing artifacts in the final image. Atstep 1108, low pass filtering and high pass filtering as applied inhorizontal and vertical directions to determine horizontal and verticalfiltering outputs. For example, the horizontal and vertical filteringoutputs, Gh and Gv, may be calculated in accordance with Equations 81and 82. Next the process 1082 continues to step 1110, at which theinterpolated green value G′ is interpolated based on the values of Ghand Gv weighted with the energy components Eh and Ev, as shown inEquation 83.

Next, with regard to the process 1112 of FIG. 114, the interpolation ofred values may begin at step 1114, at which the input pixel P isreceived (e.g., from process 1082). At step 1116, a set of neighboringpixels forming a 3×3 pixel block is identified, with P being the centerof the 3×3 block. Thereafter, low pass filtering is applied onneighboring red pixels within the 3×3 block at step 1118, and high passfiltering is applied (step 1120) on co-located green neighboring values,which may be original green values captured by the Bayer image sensor,or interpolated values (e.g., determined via process 1100 of FIG. 113).The interpolated red value R′ for P may be determined based on the lowpass and high pass filtering outputs, as shown at step 1122. Dependingon the color of P, R′ may be determined in accordance with one of theEquations 84, 86, or 88.

With regard to the interpolation of blue values, the process 1124 ofFIG. 115 may be applied. The steps 1126 and 1128 are generally identicalto the steps 1114 and 1116 of the process 1112 (FIG. 114). At step 1130,low pass filtering is applied on neighboring blue pixels within the 3×3,and, at step 1132, high pass filtering is applied on co-located greenneighboring values, which may be original green values captured by theBayer image sensor, or interpolated values (e.g., determined via process1100 of FIG. 113). The interpolated blue value B′ for P may bedetermined based on the low pass and high pass filtering outputs, asshown at step 1134. Depending on the color of P, B′ may be determined inaccordance with one of the Equations 85, 87, or 89. Further, asmentioned above, the interpolation of red and blue values may bedetermined using color differences (Equations 84-89) or color ratios(Equations 90-95). Again, it should be understood that interpolation ofmissing green values may be performed first, such that a complete set ofgreen values (both original and interpolated values) is available wheninterpolating the missing red and blue samples. For example, the process1100 of FIG. 113 may be applied to interpolate all missing green colorsamples before performing the processes 1112 and 1124 of FIGS. 114 and115, respectively.

Referring to FIGS. 116-119, examples of colored drawings of imagesprocessed by the raw pixel processing logic 900 in the ISP pipe 82 areprovided. FIG. 116 depicts an original image scene 1140, which may becaptured by the image sensor 90 of the imaging device 30. FIG. 117 showsa raw Bayer image 1142 which may represent the raw pixel data capturedby the image sensor 90. As mentioned above, conventional demosaicingtechniques may not provide for adaptive filtering based on the detectionof edges (e.g., borders between areas of two or more colors) in theimage data, which may, undesirably, produce artifacts in the resultingreconstructed full color RGB image. For instance, FIG. 118 shows an RGBimage 1144 reconstructed using conventional demosaicing techniques, andmay include artifacts, such as “checkerboard” artifacts 1146 at the edge1148. However, comparing the image 1144 to the RGB image 1150 of FIG.119, which may be an example of an image reconstructed using thedemosaicing techniques described above, it can be seen that thecheckerboard artifacts 1146 present in FIG. 118 are not present, or atleast their appearance is substantially reduced at the edge 1148. Thus,the images shown in FIGS. 116-119 are intended to illustrate at leastone advantage that the demosaicing techniques disclosed herein have overconventional methods.

In accordance with certain aspect of the image processing techniquesdisclosed herein, the various processing logic blocks of the ISPsub-system 32 may be implemented using a set of line buffers, which maybe configured to pass image data through the various blocks, as shownabove. For example, in one embodiment, the raw pixel processing logic900 discussed above in FIG. 99 may be implemented using a configurationof line buffers arranged as shown in FIGS. 120-123. Particularly, FIG.120 depicts the entire line buffer arrangement that may be used toimplement the raw pixel processing logic 900, while FIG. 121 depicts acloser view of a first subset of the line buffers, as shown within theenclosed region 1162 of FIG. 120, FIG. 122 depicts a closer view of avertical filter that may be part of the noise reduction logic 934, andFIG. 123 depicts a closer view of a second subset of the line buffers,as shown within the enclosed region 1164 of FIG. 120.

As generally illustrated in FIG. 120, the raw pixel processing logic 900may include a set of ten line buffers numbered 0-9 and labeled asreference numbers 1160 a-1160 j, respectively, as well as the row oflogic 1160 k, which includes the image data input 908 (which may be fromthe image sensor or from memory) to the raw processing logic 900. Thus,the logic shown in FIG. 120 may include 11 rows, of which 10 of the rowsinclude line buffers (1160 a-1160 j). As discussed below, the linebuffers may be utilized in a shared manner by the logic units of the rawpixel processing logic 900, including the gain, offset, clamping logicblocks 930 and 938 (referred to as GOC1 and GOC2, respectively, in FIG.120), the defective pixel detection and correction (DPC) logic 932, thenoise reduction logic 934 (shown in FIG. 120 as including the greennon-uniformity (GNU) correction logic 934 a, a 7-tap horizontal filter934 b, and a 5-tap vertical filter 934 c), the lens shading correction(LSC) logic 936, and demosaic (DEM) logic 940. For example, in theembodiment shown in FIG. 120, the lower subset of line buffersrepresented by line buffers 6-9 (1160 g-1160 j) may be shared betweenthe DPC logic 932 and portions of the noise reduction logic 934(including GNU logic 934 a, horizontal filter 934 b, and part of thevertical filter 934 c). The upper subset of line buffers represented byline buffers 0-5 (1160 a-1160 f) may be shared between a portion of thevertical filtering logic 934 c, the lens shading correction logic 936,the gain, offset, and clamping logic 938, and the demosaic logic 940.

To generally describe the movement of image data through the linebuffers, the raw image data 908, which may represent the output of theISP front-end processing logic 80, is first received and processed bythe GOCl logic 930, where appropriate gains, offset, and clampingparameters are applied. The output of the GOCl logic 930 is thenprovided to the DPC logic 932. As shown, defective pixel detection andcorrection processing may occur over line buffers 6-9. A first output ofthe DPC logic 932 is provided to the green non-uniformity correctionlogic 934 a (of the noise reduction logic 934), which occurs at linebuffer 9 (1160 j). Thus, line buffer 9 (1160 j), in the presentembodiment, is shared between both the DPC logic 932 and the GNUcorrection logic 934 a.

Next, the output of line buffer 9 (1160 j), referred to in FIG. 121 asW8, is provided to the input of line buffer 8 (1160 i). As shown, linebuffer 8 is shared between the DPC logic 932, which provides additionaldefective pixel detection and correction processing, and the horizontalfiltering logic (934 b) of the noise reduction block 934. As shown inthe present embodiment, the horizontal filter 934 b may be a 7-tapfilter, as indicated by the filter taps 1165 a-1165 g in FIG. 121, andmay be configured as a finite impulse response (FIR) filter. Asdiscussed above, in certain embodiments, the noise filtering may be edgeadaptive. For instance, the horizontal filter may be an FIR filter, butwhere the filter taps are used only if the difference between the centerpixel and the pixel at the tap is smaller then a threshold that dependsat least partially upon noise variance.

The output 1163 (FIG. 121) of the horizontal filtering logic 934 b maybe provided to the vertical filtering logic 934 c (illustrated in moredetail in FIG. 122) and to the input of line buffer 7 (1160 h). In theillustrated embodiment, line buffer 7 is configured to provide for adelay (w) before passing its input W7 to line buffer 6 (1160 g) as inputW6. As shown in FIG. 121, line buffer 6 is shared between the DPC logic932 and the noise reduction vertical filter 934 c.

Next, referring concurrently to FIGS. 120, 122, and 123, the uppersubset of line buffers, namely line buffers 0-5 (1160 a-1160 f) areshared between the noise reduction vertical filter 934 c (shown in FIG.122), the lens shading correction logic 936, the GOC2 logic 938, and thedemosaic logic 940. For instance, the output of line buffer 5 (1160 f),which provides a delay (w), is fed to line buffer 4 (1160 e). Verticalfiltering is performed in line buffer 4, and the output W3 of thevertical filter 934 c portion in line buffer 4 is fed to line buffer 3(1160 d), as well as downstream to the portions of the lens shadingcorrection logic 936, GOC2 logic 938, and demosaic logic 940 shared byline buffer 4. In the present embodiment, the vertical filtering logic934 c may include five taps 1166 a-1166 e (FIG. 122), but may beconfigurable to operate in both partially recursive (infinite impulseresponse (IIR)) and non-recursive (FIR) modes. For instance, when allfive taps are utilized such that tap 1166 c is the center tap, thevertical filtering logic 934 c operates in a partially IIR recursivemode. The present embodiment may also choose to utilize three of thefive taps, namely taps 1166 c-1166 e, with tap 1166 d being a centertap, to operate the vertical filtering logic 934 c in a non-recursive(FIR) mode. The vertical filtering mode, in one embodiment, may bespecified using a configuration register associated with the noisereduction logic 934.

Next, line buffer 3 receives the W3 input signal and provides a delay(w) before outputting W2 to line buffer 2 (1160 c), as well asdownstream to the portions of the lens shading correction logic 936,GOC2 logic 938, and demosaic logic 940 shared by line buffer 3. Asshown, line buffer 2 is also shared between the vertical filter 934 c,the lens shading correction logic 936, the GOC2 logic 938, and thedemosaic logic 940, and provides output W1 to line buffer 1 (1160 b).Similarly, line buffer 1 is also shared between the vertical filter 934c, the lens shading correction logic 936, the GOC2 logic 938, and thedemosaic logic 940, and provides output W1 to line buffer 0 (1160 a).The output 910 of the demosaic logic 940 may be provided downstream tothe RGB processing logic 902 for additional processing, as will bediscussed further below.

It should be understood that the illustrated embodiment depicting thearrangement of the line buffers in a shared manner such differentprocessing units may utilize the shared line buffers concurrently maysignificantly reduce the number of line buffers needed to implement theraw processing logic 900. As can be appreciated, this may reduce thehardware real estate area required for implementing the image processingcircuitry 32, and thus reduce overall design and manufacturing costs. Byway of example, the presently illustrated technique for sharing linebuffers between different processing components may, in certainembodiments, reduce the number of line buffers needed when compared to aconventional embodiment that does not share line buffers by as much as40 to 50 percent or more. Further, while the presently illustratedembodiment of the raw pixel processing logic 900 shown in FIG. 120utilizes 10 line buffers, it should be appreciated that fewer or moreline buffers may be utilized in other embodiments. That is, theembodiment shown in FIG. 120 is merely intended to illustrate theconcept by which line buffers are shared across multiple processingunits, and should not be construed as limiting the present technique toonly the raw pixel processing logic 900. Indeed, the aspects of thedisclosure shown in FIG. 120 may be implemented in any of the logicblocks of the ISP sub-system 32.

FIG. 124 is a flowchart showing a method 1167 for processing raw pixeldata in accordance with the line buffer configuration shown in FIGS.120-123. Beginning at step 1168, the line buffers of the raw pixelprocessing logic 900 may receive raw pixel data (e.g., from ISPfront-end 80, memory 108, or both). At step 1169, a first set of gain,offset, and clamping (GOCl) parameters is applied to the raw pixel data.Next, at step 1170, defective pixel detection and correction isperformed using a first subset of line buffers (e.g., line buffers 6-9in FIG. 120). Thereafter, at step 1171, green non-uniformity (GNU)correction is applied using at least one line buffer (e.g., line buffer9) from the first subset of line buffers. Next, as shown at step 1172,horizontal filtering for noise reduction is applied, also using at leastone line buffer from the first subset. In the embodiment shown in FIG.120, the line buffer(s) from the first subset that are used to performGNU correction and horizontal filtering may be different.

The method 1167 then continues to step 1173, at which vertical filteringfor noise reduction is applied using at least one line buffer from thefirst subset, as well as at least a portion of a second subset of theline buffers (e.g., line buffers 0-5) of the raw pixel processing logic900. For instance, as discussed above, depending on the verticalfiltering mode (e.g., recursive or non-recursive), either a portion orall of the second subset of line buffers may be used. Further, in oneembodiment, the second subset may include the remaining line buffers notincluded in the first subset of line buffers from step 1170. At step1174, the second subset of line buffers is used to apply lens shadingcorrection to the raw pixel data. Next, at step 1175, the second subsetof line buffers is used to apply a second set of gain, offset, andclamping (GOC2) parameters and, subsequently, the second set of linebuffers is also used to demosaic the raw image data, as shown at step1176. The demosaiced RGB color data may then be sent downstream at step1177 for additional processing by the RGB processing logic 902, asdiscussed in more detail below.

Referring back to FIG. 98, having now thoroughly described the operationof the raw pixel processing logic 900, which may output an RGB imagesignal 910, the present discussion will now focus on describing theprocessing of the RGB image signal 910 by the RGB processing logic 902.As shown the RGB image signal 910 may be sent to the selection logic 914and/or to the memory 108. The RGB processing logic 902 may receive theinput signal 916, which may be RGB image data from the signal 910 orfrom the memory 108, as shown by signal 912, depending on theconfiguration of the selection logic 914. The RGB image data 916 may beprocessed by the RGB processing logic 902 to perform color adjustmentsoperations, including color correction (e.g., using a color correctionmatrix), the application of color gains for auto-white balancing, aswell as global tone mapping, and so forth.

A block diagram depicting a more detailed view of an embodiment of theRGB processing logic 902 is illustrated in FIG. 125. As shown, the RGBprocessing logic 902 includes the gain, offset, and clamping (GOC) logic1178, the RGB color correction logic 1179, the GOC logic 1180, the RGBgamma adjustment logic, and the color space conversion logic 1182. Theinput signal 916 is first received by the gain, offset, and clamping(GOC) logic 1178. In the illustrated embodiment, the GOC logic 1178 mayapply gains to perform auto-white balancing on one or more of the R, G,or B color channels before processing by the color correction logic1179.

The GOC logic 1178 may be similar to the GOC logic 930 of the raw pixelprocessing logic 900, except that the color components of the RGB domainare processed, rather the R, B, Gr, and Gb components of the Bayer imagedata. In operation, the input value for the current pixel is firstoffset by a signed value O[c] and multiplied by a gain G[c], as shown inEquation 11 above, wherein c represents the R, G, and B. As discussedabove, the gain G[c] may be a 16-bit unsigned number with 2 integer bitsand 14 fraction bits (e.g., 2.14 floating point representation), and thevalues for the gain G[c] may be previously determined during statisticsprocessing (e.g., in the ISP front-end block 80). The computed pixelvalue Y (based on Equation 11) is then be clipped to a minimum and amaximum range in accordance with Equation 12. As discussed above, thevariables min[c] and max[c] may represent signed 16-bit “clippingvalues” for the minimum and maximum output values, respectively. In oneembodiment, the GOC logic 1178 may also be configured to maintain acount of the number of pixels that were clipped above and below maximumand minimum, respectively, for each color component R, G, and B.

The output of the GOC logic 1178 is then forwarded to the colorcorrection logic 1179. In accordance with the presently disclosedtechniques, the color correction logic 1179 may be configured to applycolor correction to the RGB image data using a color correction matrix(CCM). In one embodiment, the CCM may be a 3×3 RGB transform matrix,although matrices of other dimensions may also be utilized in otherembodiments (e.g., 4×3, etc.). Accordingly, the process of performingcolor correction on an input pixel having R, G, and B components may beexpressed as follows:

$\begin{matrix}{{\begin{bmatrix}R^{\prime} & G^{\prime} & B^{\prime}\end{bmatrix} = {\begin{bmatrix}{{CCM}\; 00} & {{CCM}\; 01} & {{CCM}\; 02} \\{{CCM}\; 10} & {{CCM}\; 11} & {{CCM}\; 12} \\{{CCM}\; 20} & {{CCM}\; 21} & {{CCM}\; 22}\end{bmatrix} \times \begin{bmatrix}R & G & B\end{bmatrix}}},} & (96)\end{matrix}$

wherein R, G, and B represent the current red, green, and blue valuesfor the input pixel, CCM00-CCM22 represent the coefficients of the colorcorrection matrix, and R′, G′, and B′ represent the corrected red,green, and blue values for the input pixel. Accordingly, the correctcolor values may be computed in accordance with Equations 97-99 below:

R′=(CCM00×R)+(CCM01×G)+(CCM02×B)  (97)

G′=(CCM10×R)+(CCM11×G)+(CCM12×B)  (98)

B′=(CCM20×R)+(CCM21×G)+(CCM22×B)  (99)

The coefficients (CCM00-CCM22) of the CCM may be determined duringstatistics processing in the ISP front-end block 80, as discussed above.In one embodiment, the coefficients for a given color channel may beselected such that the sum of those coefficients (e.g., CCM00, CCM01,and CCM02 for red color correction) is equal to 1, which may help tomaintain the brightness and color balance. Further, the coefficients aretypically selected such that a positive gain is applied to the colorbeing corrected. For instance, with red color correction, thecoefficient CCM00 may be greater than 1, while one or both of thecoefficients CCM01 and CCM02 may be less than 1. Setting thecoefficients in this manner may enhance the red (R) component in theresulting corrected R′ value while subtracting some of the blue (B) andgreen (G) component. As will be appreciated, this may address issueswith color overlap that may occur during acquisition of the originalBayer image, as a portion of filtered light for a particular coloredpixel may “bleed” into a neighboring pixel of a different color. In oneembodiment, the coefficients of the CCM may be provided as 16-bittwo's-complement numbers with 4 integer bits and 12 fraction bits(expressed in floating point as 4.12). Additionally, the colorcorrection logic 1179 may provide for clipping of the computed correctedcolor values if the values exceed a maximum value or are below a minimumvalue.

The output of the RGB color correction logic 1179 is then passed toanother GOC logic block 1180. The GOC logic 1180 may be implemented inan identical manner as the GOC logic 1178 and, thus, a detaileddescription of the gain, offset, and clamping functions provided willnot be repeated here. In one embodiment, the application of the GOClogic 1180 subsequent to color correction may provide for auto-whitebalance of the image data based on the corrected color values, and mayalso adjust sensor variations of the red-to-green and blue-to-greenratios.

Next, the output of the GOC logic 1180 is sent to the RGB gammaadjustment logic 1181 for further processing. For instance, the RGBgamma adjustment logic 1181 may provide for gamma correction, tonemapping, histogram matching, and so forth. In accordance with disclosedembodiments, the gamma adjustment logic 1181 may provide for a mappingof the input RGB values to corresponding output RGB values. Forinstance, the gamma adjustment logic may provide for a set of threelookup tables, one table for each of the R, G, and B components. By wayof example, each lookup table may be configured to store 256 entries of10-bit values, each value representing an output level. The tableentries may be evenly distributed in the range of the input pixelvalues, such that when the input value falls between two entries, theoutput value may be linearly interpolated. In one embodiment, each ofthe three lookup tables for R, G, and B may be duplicated, such that thelookup tables are “double buffered” in memory, thus allowing for onetable to be used during processing, while its duplicate is beingupdated. Based on the 10-bit output values discussed above, it should benoted that the 14-bit RGB image signal is effectively down-sampled to 10bits as a result of the gamma correction process in the presentembodiment.

The output of the gamma adjustment logic 1181 may be sent to the memory108 and/or to the color space conversion logic 1182. The color spaceconversion (CSC) logic 1182 may be configured to convert the RGB outputfrom the gamma adjustment logic 1181 to the YCbCr format, in which Yrepresents a luma component, Cb represents a blue-difference chromacomponent, and Cr represents a red-difference chroma component, each ofwhich may be in a 10-bit format as a result of bit-depth conversion ofthe RGB data from 14-bits to 10-bits during the gamma adjustmentoperation. As discussed above, in one embodiment, the RGB output of thegamma adjustment logic 1181 may be down-sampled to 10-bits and thusconverted to 10-bit YCbCr values by the CSC logic 1182, which may thenbe forwarded to the YCbCr processing logic 904, which will be discussedfurther below.

The conversion from the RGB domain to the YCbCr color space may beperformed using a color space conversion matrix (CSCM). For instance, inone embodiment, the CSCM may be a 3×3 transform matrix. The coefficientsof the CSCM may be set in accordance with a known conversion equation,such as the BT.601 and BT.709 standards. Additionally, the CSCMcoefficients may be flexible based on the desired range of input andoutputs. Thus, in some embodiments, the CSCM coefficients may bedetermined and programmed based on data collected during statisticsprocessing in the ISP front-end block 80.

The process of performing YCbCr color space conversion on an RGB inputpixel may be expressed as follows:

$\begin{matrix}{{\begin{bmatrix}Y & {Cb} & {Cr}\end{bmatrix} = {\begin{bmatrix}{{CSCM}\; 00} & {{CSCM}\; 01} & {{CSCM}\; 02} \\{{CSCM}\; 10} & {{CSCM}\; 11} & {{CSCM}\; 12} \\{{CSCM}\; 20} & {{CSCM}\; 21} & {{CSCM}\; 22}\end{bmatrix} \times \begin{bmatrix}R & G & B\end{bmatrix}}},} & (100)\end{matrix}$

wherein R, G, and B represent the current red, green, and blue valuesfor the input pixel in 10-bit form (e.g., as processed by the gammaadjustment logic 1181), CSCM00-CSCM22 represent the coefficients of thecolor space conversion matrix, and Y, Cb, and Cr represent the resultingluma, and chroma components for the input pixel. Accordingly, the valuesfor Y, Cb, and Cr may be computed in accordance with Equations 101-103below:

Y=(CSCM00×R)+(CSCM01×G)+(CSCM02×B)  (101)

Cb=(CSCM10×R)+(CSCM11×G)+(CSCM12×B)  (102)

Following the color space conversion operation, the resulting YCbCrvalues may be output from the CSC logic 1182 as the signal 918, whichmay be processed by the YCbCr processing logic 904, as will be discussedbelow.

In one embodiment, the coefficients of the CSCM may be 16-bittwo's-complement numbers with 4 integer bits and 12 fraction bits(4.12). In another embodiment, the CSC logic 1182 may further beconfigured to apply an offset to each of the Y, Cb, and Cr values, andto clip the resulting values to a minimum and maximum value. By way ofexample only, assuming that the YCbCr values are in 10-bit form, theoffset may be in a range of −512 to 512, and the minimum and maximumvalues may be 0 and 1023, respectively.

Referring again back to the block diagram of the ISP pipe logic 82 inFIG. 98, the YCbCr signal 918 may be sent to the selection logic 922and/or to the memory 108. The YCbCr processing logic 904 may receive theinput signal 924, which may be YCbCr image data from the signal 918 orfrom the memory 108, as shown by signal 920, depending on theconfiguration of the selection logic 922. The YCbCr image data 924 maythen be processed by the YCbCr processing logic 904 for luma sharpening,chroma suppression, chromanoise reduction, chroma noise reduction, aswell as brightness, contrast, and color adjustments, and so forth.Further, the YCbCr processing logic 904 may provide for gamma mappingand scaling of the processed image data in both horizontal and verticaldirections.

A block diagram depicting a more detailed view of an embodiment of theYCbCr processing logic 904 is illustrated in FIG. 126. As shown, theYCbCr processing logic 904 includes the image sharpening logic 1183, thelogic 1184 for adjusting brightness, contrast, and/or color, the YCbCrgamma adjustment logic 1185, the chroma decimation logic 1186, and thescaling logic 1187. The YCbCr processing logic 904 may be configured toprocess pixel data in 4:4:4, 4:2:2, or 4:2:0 formats using 1-plane,2-plane, or 3-plane memory configurations. Further, in one embodiment,the YCbCr input signal 924 may provide luma and chroma information as10-bit values.

As will be appreciated, the reference to 1-plane, 2-plane, or 3-planerefers to the number of imaging planes utilized in picture memory. Forinstance, in a 3-plane format, each of the Y, Cb, and Cr components mayutilize separate respective memory planes. In a 2-plane format, a firstplane may be provided for the luma component (Y), and a second planethat interleaves the Cb and Cr samples may be provided for the chromacomponents (Cb and Cr). In a 1-plane format, a single plane in memory isinterleaved with the luma and chroma samples. Further, with regard tothe 4:4:4, 4:2:2, and 4:2:0 formats, it may be appreciated that the4:4:4 format refers to a sampling format in which each of the threeYCbCr components are sampled at the same rate. In a 4:2:2 format, thechroma components Cb and Cr are sub-sampled at half the sampling rate ofthe luma component Y, thus reducing the resolution of chroma componentsCb and Cr by half in the horizontal direction. Similarly the 4:2:0format subs-samples the chroma components Cb and Cr in both the verticaland horizontal directions.

The processing of the YCbCr information may occur within an activesource region defined within a source buffer, wherein the active sourceregion contains “valid” pixel data. For example, referring to FIG. 127,a source buffer 1188 having defined therein an active source region 1189is illustrated. In the illustrated example, the source buffer mayrepresent a 4:4:4 1-plane format providing source pixels of 10-bitvalues. The active source region 1189 may be specified individually forluma (Y) samples and chroma samples (Cb and Cr). Thus, it should beunderstood that the active source region 1189 may actually includemultiple active source regions for the luma and chroma samples. Thestart of the active source regions 1189 for luma and chroma may bedetermined based on an offset from a base address (0,0) 1190 of thesource buffer. For instance, a starting position (Lm_X, Lm_Y) 1191 forthe luma active source region may be defined by an x-offset 1193 and ay-offset 1196 with respect to the base address 1190. Similarly, astarting position (Ch_X, Ch_Y) 1192 for the chroma active source regionmay be defined by an x-offset 1194 and a y-offset 1198 with respect tothe base address 1190. It should be noted that in the present example,the y-offsets 1196 and 1198 for luma and chroma, respectively, may beequal. Based on the starting position 1191, the luma active sourceregion may be defined by a width 1195 and a height 1200, each of whichmay represent the number of luma samples in the x and y directions,respectively. Additionally, based on the starting position 1192, thechroma active source region may be defined by a width 1202 and a height1204, each of which may represent the number of chroma samples in the xand y directions, respectively.

FIG. 128 further provides an example showing how active source regionsfor luma and chroma samples may be determined in a two-plane format. Forinstance, as shown, the luma active source region 1189 may be defined ina first source buffer 1188 (having the base address 1190) by the areaspecified by the width 1195 and height 1200 with respect to the startingposition 1191. A chroma active source region 1208 may be defined in asecond source buffer 1206 (having the base address 1190) as the areaspecified by the width 1202 and height 1204 relative to the startingposition 1192.

With the above points in mind and referring back to FIG. 126, the YCbCrsignal 924 is first received by the image sharpening logic 1183. Theimage sharpening logic 1183 may be configured to perform picturesharpening and edge enhancement processing to increase texture and edgedetails in the image. As will be appreciated, image sharpening mayimprove the perceived image resolution. However, it is generallydesirable that existing noise in the image is not detected as textureand/or edges, and thus not amplified during the sharpening process.

In accordance with the present technique, the image sharpening logic1183 may perform picture sharpening using a multi-scale unsharp maskfilter on the luma (Y) component of the YCbCr signal. In one embodiment,two or more low pass Gaussian filters of difference scale sizes may beprovided. For example, in an embodiment that provides two Gaussianfilters, the output (e.g., Gaussian blurring) of a first Gaussian filterhaving a first radius (x) is subtracted from the output of a secondGaussian filter having a second radius (y), wherein x is greater than y,to generate an unsharp mask. Additional unsharp masks may also beobtained by subtracting the outputs of the Gaussian filters from the Yinput. In certain embodiments, the technique may also provide adaptivecoring threshold comparison operations that may be performed using theunsharp masks such that, based upon the results of the comparison(s),gain amounts may be added to a base image, which may be selected as theoriginal Y input image or the output of one of the Gaussian filters, togenerate a final output.

Referring to FIG. 129, block diagram depicting exemplary logic 1210 forperforming image sharpening in accordance with embodiments of thepresently disclosed techniques is illustrated. The logic 1210 representsa multi-scale unsharp filtering mask that may be applied to an inputluma image Yin. For instance, as shown, Yin is received and processed bytwo low pass Gaussian filters 1212 (G1) and 1214 (G2). In the presentexample, the filter 1212 may be a 3×3 filter and the filter 1214 may bea 5×5 filter. It should be appreciated, however, that in additionalembodiments, more than two Gaussian filters, including filters ofdifferent scales may also be used (e.g., 7×7, 9×9, etc.). As will beappreciated, due to the low pass filtering process, the high frequencycomponents, which generally correspond to noise, may be removed from theoutputs of the G1 and G2 to produce “unsharp” images (G1out and G2out).As will be discussed below, using an unsharp input image as a base imageallows for noise reduction as part of the sharpening filter.

The 3×3 Gaussian filter 1212 and the 5×5 Gaussian filter 1214 may bedefined as shown below:

${G\; 1} = {{\frac{\begin{bmatrix}{G\; 1_{1}} & {G\; 1_{1}} & {G\; 1_{1}} \\{G\; 1_{1}} & {G\; 1_{0}} & {G\; 1_{1}} \\{G\; 1_{1}} & {G\; 1_{1}} & {G\; 1_{1}}\end{bmatrix}}{256}\mspace{14mu} G\; 2} = \frac{\begin{bmatrix}{G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{0}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}}\end{bmatrix}}{256}}$

By way of example only, the values of the Gaussian filters G1 and G2 maybe selected in one embodiment as follows:

${G\; 1} = {{\frac{\begin{bmatrix}28 & 28 & 28 \\28 & 28 & 28 \\28 & 28 & 28\end{bmatrix}}{256}\mspace{14mu} G\; 2} = \frac{\begin{bmatrix}9 & 9 & 9 & 9 & 9 \\9 & 12 & 12 & 12 & 9 \\9 & 12 & 16 & 12 & 9 \\9 & 12 & 12 & 12 & 9 \\9 & 9 & 9 & 9 & 9\end{bmatrix}}{256}}$

Based on Yin, G1out, and G2out, three unsharp masks, Sharp1, Sharp2, andSharp3, may be generated. Sharp1 may be determined as the unsharp imageG2out of the Gaussian filter 1214 subtracted from the unsharp imageG1out of the Gaussian filter 1212. Because Sharp1 is essentially thedifference between two low pass filters, it may be referred to as a “midband” mask, since the higher frequency noise components are alreadyfiltered out in the G1out and G2out unsharp images. Additionally, Sharp2may be calculated by subtracting G2out from the input luma image Yin,and Sharp3 may be calculated by subtracting G1out from the input lumaimage Yin. As will be discussed below, an adaptive threshold coringscheme may be applied using the unsharp masks Sharp1, Sharp2, andSharp3.

Referring to the selection logic 1216, a base image may be selectedbased upon a control signal UnsharpSe1. In the illustrated embodiment,the base image may be either the input image Yin, or the filteredoutputs G1out or G2out. As will be appreciated, when an original imageshas a high noise variance (e.g., almost as high as the signal variance),using the original image Yin as the base image in sharpening may notsufficiently provide for reduction of the noise components duringsharpening. Accordingly, when a particular threshold of noise content isdetected in the input image, the selection logic 1216 may be adapted toselect one of the low pass filtered outputs G1out or G2out from whichhigh frequency content, which may include noise, has been reduced. Inone embodiment, the value of the control signal UnsharpSe1 may bedetermined by analyzing statistical data acquired during statisticsprocessing in the ISP front-end block 80 to determine the noise contentof the image. By way of example, if the input image Yin has a low noisecontent, such that the appearance noise will likely not increase as aresult of the sharpening process, the input image Yin may be selected asthe base image (e.g., UnsharpSe1=j). If the input image Yin isdetermined to contain a noticeable level of noise, such that thesharpening process may amplify the noise, one of the filtered imagesG1out or G2out may be selected (e.g., UnsharpSe1=1 or 2, respectively).Thus, by applying an adaptive technique for selecting a base image, thelogic 1210 essentially provides a noise reduction function.

Next, gains may be applied to one or more of the Sharp1, Sharp2, andSharp3 masks in accordance with an adaptive coring threshold scheme, asdescribed below. Next, the unsharp values Sharp1, Sharp2, and Sharp3 maybe compared to various thresholds SharpThd1, SharpThd2, and SharpThd3(not necessarily respectively) by way of the comparator blocks 1218,1220, and 1222. For instance, Sharp1 value is always compared toSharpThd1 at the comparator block 1218. With respective to thecomparator block 1220, the threshold SharpThd2 may be compared againsteither Sharp1 or Sharp2, depending upon the selection logic 1226. Forinstance, the selection logic 1226 may select Sharp1 or Sharp2 dependingon the state of a control signal SharpCmp2 (e.g., SharpCmp2=1 selectsSharp1; SharpCmp2=0 selects Sharp2). For example, in one embodiment, thestate of SharpCmp2 may be determined depending on the noisevariance/content of the input image (Yin).

In the illustrated embodiment, it is generally preferable to set theSharpCmp2 and SharpCmp3 values to select Sharp1, unless it is detectedthat the image data has relatively low amounts of noise. This is becauseSharp1, being the difference between the outputs of the Gaussian lowpass filters G1 and G2, is generally less sensitive to noise, and thusmay help reduce the amount to which SharpAmt1, SharpAmt2, and SharpAmt3values vary due to noise level fluctuations in “noisy” image data. Forinstance, if the original image has a high noise variance, some of thehigh frequency components may not be caught when using fixed thresholdsand, thus, may be amplified during the sharpening process. Accordingly,if the noise content of the input image is high, then some of the noisecontent may be present in Sharp2. In such instances, SharpCmp2 may beset to 1 to select the mid-band mask Sharp1 which, as discussed above,has reduced high frequency content due to being the difference of twolow pass filter outputs and is thus less sensitive to noise.

As will be appreciated, a similar process may be applied to theselection of either Sharp1 or Sharp3 by the selection logic 1224 underthe control of SharpCmp3. In one embodiment, SharpCmp2 and SharpCmp3 maybe set to 1 by default (e.g., use Sharp1), and set to 0 only for thoseinput images that are identified as having generally low noisevariances. This essentially provides an adaptive coring threshold schemein which the selection of the comparison value (Sharp1, Sharp2, orSharp3) is adaptive based upon the noise variance of an input image.

Based on the outputs of the comparator blocks 1218, 1220, and 1222, thesharpened output image Ysharp may be determined by applying gainedunsharp masks to the base image (e.g., selected via logic 1216). Forinstance, referring first to the comparator block 1222, SharpThd3 iscompared to the B-input provided by selection logic 1224, which shall bereferred to herein as “SharpAbs,” and may be equal to either Sharp1 orSharp3 depending on the state of SharpCmp3. If SharpAbs is greater thanthe threshold SharpThd3, then a gain SharpAmt3 is applied to Sharp3, andthe resulting value is added to the base image. If SharpAbs is less thanthe threshold SharpThd3, then an attenuated gain Att3 may be applied. Inone embodiment, the attenuated gain Att3 may be determined as follows:

$\begin{matrix}{{{Att}\; 3} = \frac{{SharpAmt}\; 3 \times {SharpAbs}}{{SharpThd}\; 3}} & (104)\end{matrix}$

wherein, SharpAbs is either Sharp1 or Sharp3, as determined by theselection logic 1224. The selection of the based image summed witheither the full gain (SharpAmt3) or the attenuated gain (Att3) isperformed by the selection logic 1228 based upon the output of thecomparator block 1222. As will be appreciated, the use of an attenuatedgain may address situations in which SharpAbs is not greater than thethreshold (e.g., SharpThd3), but the noise variance of the image isnonetheless close to the given threshold. This may help to reducenoticeable transitions between a sharp and an unsharp pixel. Forinstance, if the image data is passed without the attenuated gain insuch circumstance, the resulting pixel may appear as a defective pixel(e.g., a stuck pixel).

Next, a similar process may be applied with respect to the comparatorblock 1220. For instance, depending on the state of SharpCmp2, theselection logic 1226 may provide either Sharp1 or Sharp2 as the input tothe comparator block 1220 that is compared against the thresholdSharpThd2. Depending on the output of the comparator block 1220, eitherthe gain SharpAmt2 or an attenuated gain based upon SharpAmt2, Att2, isapplied to Sharp2 and added to the output of the selection logic 1228discussed above. As will be appreciated, the attenuated gain Att2 may becomputed in a manner similar to Equation 104 above, except that the gainSharpAmt2 and the threshold SharpThd2 are applied with respect toSharpAbs, which may be selected as Sharp1 or Sharp2.

Thereafter, a gain SharpAmt1 or an attenuated gain Att1 is applied toSharp1, and the resulting value is summed with output of the selectionlogic 1230 to produce the sharpened pixel output Ysharp (from selectionlogic 1232). The selection of applying either the gain SharpAmt1 orattenuated gain Att1 may be determined based upon the output of thecomparator block 1218, which compares Sharp1 against the thresholdSharpThd1. Again, the attenuated gain Att1 may be determined in a mannersimilar to Equation 104 above, except that the gain SharpAmt1 andthreshold SharpThd1 are applied with respect to Sharp1. The resultingsharpened pixel values scaled using each of the three masks is added tothe input pixel Yin to generate the sharpened output Ysharp which, inone embodiment, may be clipped to 10 bits (assuming YCbCr processingoccurs at 10-bit precision).

As will be appreciated, when compared to conventional unsharp maskingtechniques, the image sharpening techniques set forth in this disclosuremay provide for improving the enhancement of textures and edges whilealso reducing noise in the output image. In particular, the presenttechniques may be well-suited in applications in which images capturedusing, for example, CMOS image sensors, exhibit poor signal-to-noiseratio, such as images acquired under low lighting conditions using lowerresolution cameras integrated into portable devices (e.g., mobilephones). For instance, when the noise variance and signal variance arecomparable, it is difficult to use a fixed threshold for sharpening, assome of the noise components would be sharpened along with texture andedges. Accordingly, the techniques provided herein, as discussed above,may filter the noise from the input image using multi-scale Gaussianfilters to extract features from the unsharp images (e.g., G1out andG2out) in order to provide a sharpened image that also exhibits reducednoise content.

Before continuing, it should be understood that the illustrated logic1210 is intended to provide only one exemplary embodiment of the presenttechnique. In other embodiments, additional or fewer features may beprovided by the image sharpening logic 1183. For instance, in someembodiments, rather than applying an attenuated gain, the logic 1210 maysimply pass the base value. Additionally, some embodiments may notinclude the selection logic blocks 1224, 1226, or 1216. For instance,the comparator blocks 1220 and 1222 may simply receive the Sharp2 andSharp3 values, respectively, rather than a selection output from theselection logic blocks 1224 and 1226, respectively. While suchembodiments may not provide for sharpening and/or noise reductionfeatures that are as robust as the implementation shown in FIG. 129, itshould be appreciated that such design choices may be the result of costand/or business related constraints.

In the present embodiment, the image sharpening logic 1183 may alsoprovide for edge enhancement and chroma suppression features once thesharpened image output YSharp is obtained. Each of these additionalfeatures will now be discussed below. Referring first to FIG. 130,exemplary logic 1234 for performing edge enhancement that may beimplemented downstream from the sharpening logic 1210 of FIG. 129 isillustrated in accordance with one embodiment. As shown, the originalinput value Yin is processed by a Sobel filter 1236 for edge detection.The Sobel filter 1236 may determine a gradient value YEdge based upon a3×3 pixel block (referred to as “A” below) of the original image, withYin being the center pixel of the 3×3 block. In one embodiment, theSobel filter 1236 may calculate YEdge by convolving the original imagedata to detect changes in horizontal and vertical directions. Thisprocess is shown below in Equations 105-107.

$\begin{matrix}{S_{x} = {{\begin{bmatrix}1 & 0 & {- 1} \\2 & 0 & {- 2} \\1 & 0 & {- 1}\end{bmatrix}\mspace{31mu} S_{y}} = \begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix}}} & \; \\{{G_{x} = {S_{x} \times A}},} & (105) \\{{G_{y} = {S_{y} \times A}},} & (106) \\{{{YEdge} = {G_{x} \times G_{y}}},} & (107)\end{matrix}$

wherein S_(X) and S₃, are represent matrix operators for gradientedge-strength detection in the horizontal and vertical directions,respectively, and wherein G_(x) and G_(y) represent gradient images thatcontain horizontal and vertical change derivatives, respectively.Accordingly, the output YEdge is determined as the product of G_(x) andG_(y).

YEdge is then received by selection logic 1240 along with the mid-bandSharp1 mask, as discussed above in FIG. 129. Based on the control signalEdgeCmp, either Sharp1 or YEdge is compared to a threshold, EdgeThd, atthe comparator block 1238. The state of EdgeCmp may be determined, forexample, based upon the noise content of an image, thus providing anadaptive coring threshold scheme for edge detection and enhancement.Next, the output of the comparator block 1238 may be provided to theselection logic 1242 and either a full gain or an attenuated gain may beapplied. For instance, when the selected B-input to the comparator block1238 (Sharp1 or YEdge) is above EdgeThd, YEdge is multiplied by an edgegain, EdgeAmt, to determine the amount of edge enhancement that is to beapplied. If the B-input at the comparator block 1238 is less thanEdgeThd, then an attenuated edge gain, AttEdge, may be applied to avoidnoticeable transitions between the edge enhanced and original pixel. Aswill be appreciated, AttEdge may be calculated in a similar manner asshown in Equation 104 above, but wherein EdgeAmt and EdgeThd are appliedto “SharpAbs,” which may be Sharp1 or YEdge, depending on the output ofthe selection logic 1240. Thus, the edge pixel, enhanced using eitherthe gain (EdgeAmt) or the attenuated gain (AttEdge) may be added toYSharp (output of logic 1210 of FIG. 129) to obtain the edge-enhancedoutput pixel Yout which, in one embodiment, may be clipped to 10 bits(assuming YCbCr processing occurs at 10-bit precision).

With regard to chroma suppression features provided by the imagesharpening logic 1183, such features may attenuate chroma at luma edges.Generally, chroma suppression may be performed by applying a chroma gain(attenuation factor) of less than 1 depending on the value (YSharp,Yout) obtained from the luma sharpening and/or edge enhancement stepsdiscussed above. By way of example, FIG. 131 shows a graph 1250 thatincludes a curve 1252 representing chroma gains that may be selected forcorresponding sharpened luma values (YSharp). The data represented bythe graph 1250 may be implemented as a lookup table of YSharp values andcorresponding chroma gains between 0 and 1 (an attenuation factor). Thelookup tables are used to approximate the curve 1252. For YSharp valuesthat are co-located between two attenuation factors in the lookup table,linear interpolation may be applied to the two attenuation factorscorresponding to YSharp values above and below the current YSharp value.Further, in other embodiments, the input luma value may also be selectedas one of the Sharp1, Sharp2, or Sharp3 values determined by the logic1210, as discussed above in FIG. 129, or the YEdge value determined bythe logic 1234, as discussed in FIG. 130.

Next, the output of the image sharpening logic 1183 (FIG. 126) isprocessed by the brightness, contrast, and color (BCC) adjustment logic1184. A functional block diagram depicting an embodiment of the BCCadjustment logic 1184 is illustrated in FIG. 132. As shown the logic1184 includes a brightness and contrast processing block 1262, globalhue control block 1264, and a saturation control block 1266. Thepresently illustrated embodiment provides for processing of the YCbCrdata in 10-bit precision, although other embodiments may utilizedifferent bit-depths. The functions of each of blocks 1262, 1264, and1266 are discussed below.

Referring first to the brightness and contrast processing block 1262, anoffset, YOffset, is first subtracted from the luma (Y) data to set theblack level to zero. This is done to ensure that the contrast adjustmentdoes not alter the black levels. Next, the luma value is multipled by acontrast gain value to apply contrast control. By way of example, thecontrast gain value may be a 12-bit unsigned with 2 integer bits and 10fractional bits, thus providing for a contrast gain range of up to 4times the pixel value. Thereafter, brightness adjustment may beimplemented by adding (or subtracting) a brightness offset value fromthe luma data. By way of example, the brightness offset in the presentembodiment may be a 10-bit two's complement value having a range ofbetween −512 to +512. Further, it should be noted that brightnessadjustment is performed subsequent to contrast adjustment in order toavoid varying the DC offset when changing contrast. Thereafter, theinitial YOffset is added back to the adjusted luma data to re-positionthe black level.

Blocks 1264 and 1266 provide for color adjustment based upon huecharacteristics of the Cb and Cr data. As shown, an offset of 512(assuming 10-bit processing) is first subtracted from the Cb and Cr datato position the range to approximately zero. The hue is then adjusted inaccordance with the following equations:

Cb_(adj)=Cb cos(θ)+Cr sin(θ),  (108)

Cr_(adj)=Cr cos(θ)−Cb sin(θ),  (109)

wherein Cb_(adj) and Cr_(adj) represent adjusted Cb and Cr values, andwherein θ represents a hue angle, which may be calculated as follows:

$\begin{matrix}{\theta = {\arctan \left( \frac{Cr}{Cb} \right)}} & (110)\end{matrix}$

The above operations are depicted by the logic within the global huecontrol block 1264, and may be represented by the following matrixoperation:

$\begin{matrix}{{\begin{bmatrix}{Cb}_{adj} \\{Cr}_{adj}\end{bmatrix} = {\begin{bmatrix}{Ka} & {Kb} \\{- {Kb}} & {Ka}\end{bmatrix}\begin{bmatrix}{Cb} \\{Cr}\end{bmatrix}}},} & (111)\end{matrix}$

wherein, Ka=cos(θ), Kb=sin(θ), and θ is defined above in Equation 110.

Next, saturation control may be applied to the Cb_(adj) and Cr_(adj)values, as shown by the saturation control block 1266. In theillustrated embodiment, saturation control is performed by applying aglobal saturation multiplier and a hue-based saturation multiplier foreach of the Cb and Cr values. Hue-based saturation control may improvethe reproduction of colors. The hue of the color may be represented inthe YCbCr color space, as shown by the color wheel graph 1270 in FIG.133. As will be appreciated, the YCbCr hue and saturation color wheel1270 may be derived by shifting the identical color wheel in the HSVcolor space (hue, saturation, and intensity) by approximately 109degrees. As shown, the graph 1270 includes circumferential valuesrepresenting the saturation multiplier (S) within a range of 0 to 1, aswell as angular values representing θ, as defined above, within a rangeof between 0 to 360°. Each θ may represent a different color (e.g.,49°=magenta, 109°=red, 229°=green, etc.). The hue of the color at aparticular hue angle θ may be adjusted by selecting an appropriatesaturation multiplier S.

Referring back to FIG. 132, the hue angle θ (calculated in the globalhue control block 1264) may be used as an index for a Cb saturationlookup table 1268 and a Cr saturation lookup table 1269. In oneembodiment, the saturation lookup tables 1268 and 1269 may contain 256saturation values distributed evenly in the hue range from 0-360° (e.g.,the first lookup table entry is at 0° and the last entry is at 360°) andthe saturation value S at a given pixel may be determined via linearinterpolation of saturation values in the lookup table just below andabove the current hue angle θ. A final saturation value for each of theCb and Cr components is obtained by multiplying a global saturationvalue (which may be a global constant for each of Cb and Cr) with thedetermined hue-based saturation value. Thus, the final corrected Cb′ andCr′ values may be determined by multiplying Cb_(adj) and Cr_(adj) withtheir respective final saturation values, as shown in the hue-basedsaturation control block 1266.

Thereafter, the output of the BCC logic 1184 is passed to the YCbCrgamma adjustment logic 1185, as shown in FIG. 126. In one embodiment,the gamma adjustment logic 1185 may provide non-linear mapping functionsfor the Y, Cb and Cr channels. For instance, the input Y, Cb, and Crvalues are mapped to corresponding output values. Again, assuming thatthe YCbCr data is processed in 10-bits, an interpolated 10-bit 256 entrylookup table may be utilized. Three such lookup tables may be providedwith one for each of the Y, Cb, and Cr channels. Each of the 256 inputentries may be evenly distributed and, an output may be determined bylinear interpolation of the output values mapped to the indices justabove and below the current input index. In some embodiments, anon-interpolated lookup table having 1024 entries (for 10-bit data) mayalso be used, but may have significantly greater memory requirements. Aswill be appreciated, by adjusting the output values of the lookuptables, the YCbCr gamma adjustment function may be also be used toperform certain image filter effects, such as black and white, sepiatone, negative images, solarization, and so forth.

Next, chroma decimation may be applied by the chroma decimation logic1186 to the output of the gamma adjustment logic 1185. In oneembodiment, the chroma decimation logic 1186 may be configured toperform horizontal decimation to convert the YCbCr data from a 4:4:4format to a 4:2:2 format, in which the chroma (Cr and Cr) information issub-sampled at half rate of the luma data. By way of example only,decimation may be performed by applying a 7-tap low pass filter, such asa half-band lanczos filter, to a set of 7 horizontal pixels, as shownbelow:

$\begin{matrix}{{{Out} = \frac{\begin{matrix}{{C\; 0 \times {{in}\left( {i - 3} \right)}} + {C\; 1 \times {in}\left( {i - 2} \right)} + {C\; 2 \times {{in}\left( {i - 1} \right)}} + {C\; 3 \times {{in}(i)}} +} \\{{C\; 4 \times {{in}\left( {i + 1} \right)}} + {C\; 5 \times {{in}\left( {i + 2} \right)}} + {C\; 6 \times {{in}\left( {i + 3} \right)}}}\end{matrix}}{512}},} & (112)\end{matrix}$

wherein in(i) represents the input pixel (Cb or Cr), and C0-C6 representthe filtering coefficients of the 7-tap filter. Each input pixel has anindependent filter coefficient (C0-C6) to allow flexible phase offsetfor the chroma filtered samples.

Further, chroma decimation may, in some instances, also be performedwithout filtering. This may be useful when the source image wasoriginally received in 4:2:2 format, but was up-sampled to 4:4:4 formatfor YCbCr processing. In this case, the resulting decimated 4:2:2 imageis identical to the original image.

Subsequently, the YCbCr data output from the chroma decimation logic1186 may be scaled using the scaling logic 1187 prior to being outputfrom the YCbCr processing block 904. The function of the scaling logic1187 may be similar to the functionality of the scaling logic 709, 710in the binning compensation filter 652 of the front-end pixel processingunit 150, as discussed above with reference to FIG. 59. For instance,the scaling logic 1187 may perform horizontal and vertical scaling astwo steps. In one embodiment, a 5-tap polyphase filter may be used forvertical scaling, and a 9-tap polyphase filter may be used forhorizontal scaling. The multi-tap polyphase filters may multiply pixelsselected from the source image by a weighting factor (e.g., filtercoefficient), and then sum the outputs to form the destination pixel.The selected pixels may be chosen depending on the current pixelposition and the number of filters taps. For instance, with a vertical5-tap filter, two neighboring pixels on each vertical side of a currentpixel may be selected and, with a horizontal 9-tap filter, fourneighboring pixels on each horizontal side of the current pixel may beselected. The filtering coefficients may be provided from a lookuptable, and may be determined by the current between-pixel fractionalposition. The output 926 of the scaling logic 1187 is then output fromthe YCbCr processing block 904.

Returning back to FIG. 98, the processed output signal 926 may be sentto the memory 108, or, in accordance with the embodiment of the imageprocessing circuitry 32 shown in FIG. 7, may be output from the ISP pipeprocessing logic 82 as the image signal 114 to display hardware (e.g.,display 28) for viewing by a user, or to a compression engine (e.g.,encoder 118). In some embodiments, the image signal 114 may be furtherprocessed by a graphics processing unit and/or a compression engine andstored before being decompressed and provided to a display.Additionally, one or more frame buffers may also be provided to controlthe buffering of the image data being output to a display, particularlywith respect to video image data. Further, in an embodiment where theISP back-end processing logic 120 is provided (e.g., FIG. 8), the imagesignal 114 may be sent downstream for additional post-processing steps,as will be discussed in the following section.

The ISP Back-End Processing Logic

Having described the ISP front-end logic 80 and ISP pipeline 82 indetail above, the present discussion will now shift focus to the ISPback-end processing logic 120, which is depicted above in FIG. 8. Asdiscussed above, the ISP back-end logic 120 generally functions toreceive processed image data provided by the ISP pipeline 82 or frommemory 108 (signal 124), and to perform additional image post-processingoperations, i.e., prior to outputting the image data to the displaydevice 28.

A block diagram showing an embodiment of the ISP back-end logic 120 isdepicted in FIG. 134. As illustrated, the ISP back-end processing logic120 may include feature detection logic 2200, local tone mapping logic(LTM) 2202, brightness, contrast, and color adjustment logic 2204,scaling logic 2206, and a back-end statistics unit 2208. The featuredetection logic 2200 may include face detection logic in one embodiment,and may be configured to identify the location(s) of faces/facialfeatures in an image frame, shown here by reference number 2201. Inother embodiments, the feature detection logic 2200 may also beconfigured to detect the locations of other types of features, such ascorners of objects in the image frame. For example, this data may beused to identify the location of features in consecutive image frames inorder to determine an estimation of global motion between frames, whichmay then be used to perform certain image processing operations, such asimage registration. In one embodiment, the identification of cornerfeatures and the like may be particularly useful for algorithms thatcombine multiple image frames, such as in certain high dynamic range(HDR) imaging algorithms, as well as certain panoramic stitchingalgorithms.

For simplicity, the feature detection logic 2200 will be referred to inthe description below as being face detection logic. It should beunderstood, however, that the logic 2200 is not intended limited to justface detection logic, and may be configured to detect other types offeatures instead of or in addition to facial features. For instance, inone embodiment, the logic 2200 may detect corner features, as discussedabove, and the output 2201 of the feature detection logic 2200 mayinclude corner features.

The face detection logic 2200 may be configured to receive YCC imagedata 114 provided by the ISP pipeline 82 or may receive a reducedresolution image (represented by signal 2207) from the scaling logic2206, and to detect the location and positions of faces and/or facialfeatures within the image frame corresponding to the selected imagedata. As shown in FIG. 134, the input to the face detection logic 2200may include a selection circuit 2196 that receives the YCC image data114 from the ISP pipeline 82 and the reduced resolution image 2207 fromthe scaling logic 2206. A control signal, which may be provided by theISP control logic 84 (e.g., a processor executing firmware), maydetermine which input is provided to the face detection logic 2200.

The detected location of faces/facial features, represented here bysignal 2201, may be provided as feedback data to one or more upstreamprocessing units, as well as one or more downstream units. By way ofexample, the data 2201 may represent locations in which faces or facialfeatures appear within the present image frame. In some embodiments, thedata 2201 may include a reduced resolution transform image, which mayprovide additional information for face detection. Further, the facedetection logic 2200, in some embodiments, may utilize a facialdetection algorithm, such as the Viola-Jones facial/object detectionalgorithm, or may utilize any other algorithm, transform, or patterndetection/matching techniques suitable for the detection of facialfeatures in an image.

In the illustrated embodiment, the face detection data 2201 may be fedback to control logic 84, which may represent a processor executingfirmware for controlling the image processing circuitry 32. The controllogic 84, in one embodiment, may provide the data 2201 to the front-endstatistics control loop (e.g., including the front-end statisticsprocessing units (142 and 144) of the ISP front-end 80 logic of FIG.10), whereby the statistics processing units 142 or 144 may utilize thefeedback data 2201 to position the appropriate window(s) and/or selectparticular tiles for auto-white balance, auto-exposure, and auto-focusprocessing. As will be appreciated, improving the color and/or toneaccuracy for areas of an image that contain facial features may resultin an image that appears more aesthetically pleasing to a viewer. Aswill be discussed further below, the data 2201 may also be provided tothe LTM logic 2202, the back-end statistics unit 2208, as well as to theencoder/decoder block 118.

The LTM logic 2202 may also receive the YCC image data 114 from the ISPpipeline 82. As discussed above, the LTM logic 2202 may be configured toapply tone mapping to the image data 114. As will be appreciated, tonemapping techniques may be utilized in image processing applications tomap one set of pixel values to another. In instances where the input andoutput images have the same bit precision, tone mapping may not benecessary, although some embodiments may apply tone mapping withoutcompression in order to improve contrast characteristics in the outputimage (e.g., to make bright areas appear darker and dark areas appearbrighter). However, when the input and output images have different bitprecisions, tone mapping may be applied to map the input image values tocorresponding values of the output range of the input image. Forinstance, scenes may have a dynamic range of 25,000:1 or more, whilecompression standards may allow for a much lower range (e.g., 256:1) fordisplay purposes, and sometimes an even lower range (e.g., 100:1) forprinting.

Thus, by way of example only, tone mapping may be useful in a situation,such as when image data expressed as to a precision of 10-bits or moreis to be output in a lower precision format, such as an 8-bit JPEGimage. Additionally, tone mapping may be particularly useful whenapplied to high dynamic range (HDR) images. In digital image processing,HDR images may be generated by acquiring multiple images of a scene atdifferent exposure levels and combining or compositing the images togenerate an image that has a dynamic range which is higher than can beachieved using a single exposure. Further, in some imaging systems, animage sensor (e.g., sensor 90 a, 90 b) may be configured to acquire HDRimages without the need for combining multiple images to generate acomposite HDR image.

The LTM logic 2202 of the illustrated embodiment may utilize local tonemapping operators (e.g., spatially varying), which may be determinedbased on local features within the image frame. For instance, local tonemapping operators may be region-based, and may change locally based onthe content within a particular region of the image frame. By way ofexample only, local tone mapping operators may be based on gradientdomain HDR compression, photographic tone reproduction, or Retinex®image processing.

As can be appreciated, local tone mapping techniques, when applied toimages, may generally produce output images having improved contrastcharacteristics and may appear more aesthetically pleasing to a viewerrelative to images processed using global tone mapping. FIGS. 135 and136 illustrate some of the drawbacks associated with global tonemapping. For instance, referring to FIG. 135, the graph 2400 representsthe tone mapping of input image having an input range 2401 to an outputrange 2403. The range of tone in the input image is represented by thecurve 2402, wherein the values 2404 represent bright areas of the imageand the values 2406 represent dark areas of the image.

By way of example, in one embodiment, the range 2401 of the input imagemay have 12-bit precision (0-4095), and may be mapped to an output range2403 having 8-bit precision (0-255, e.g., a JPEG image). FIG. 135 showsa linear tone mapping process, in which the curve 2402 is linearlymapped to the curve 2410. As illustrated, the result of the tone mappingprocess shown in FIG. 135 results in the range 2404 corresponding tobright areas of the input image being compressed to a smaller range2412, and also results in the range 2406 corresponding to dark areas ofthe input image being compressed to a smaller range 2414. The reductionin the tone range for dark areas (e.g., shadows) and bright areas maynegatively impact contrast properties, and may appear aestheticallyunpleasing to a viewer.

Referring to FIG. 136, one method to address the problems associatedwith the compression of the “bright” range 2404 (compressed to range2412) and the “dark” range 2406 (compressed to range 2414), as shown inFIG. 176A, is to use a non-linear tone mapping technique. For instance,in FIG. 136, the tone curve 2402 representing the input image is mappedusing a non-linear “S”-shaped curve (or S-curve) 2422. As a result ofthe non-linear mapping, the bright portion of the input range 2404 ismapped to the bright portion of the output range 2424 and, similarly,the dark portion of the input range 2406 is mapped to the dark portionof the output range 2426. As shown, the bright and dark ranges 2424 and2426 of the output image of FIG. 136 are greater than the bright anddark ranges 2412 and 2414 of the output image of FIG. 135, and thuspreserve more of the bright and dark content of the input image.However, due to the non-linear (e.g., S-curve) aspect of the mappingtechnique of FIG. 136, the mid-range values 2428 of the output image mayappear flatter, which may also be aesthetically unpleasing to a viewer.

Accordingly, embodiments of the present disclosure may implement localtone mapping techniques using local tone mapping operators to processdiscrete sections of the current image frame, which may be divided intoregions based local features within the image, such as brightnesscharacteristics. For instance, as shown in FIG. 137, a portion 2430 ofthe image frame received by the ISP back-end logic 120 may include abright region 2432 and a dark region 2434. By way of example, the brightregion 2432 may represent a light area of the image, such as a sky orhorizon, whereas the dark area may represent a relatively darker area ofthe image, such as a foreground or landscape. Local tone mapping may beapplied separately for each of the regions 2432 and 2434 to produce anoutput image that preserves more of the dynamic range of the input imagerelative to the above-discussed global tone mapping techniques, thusimproving local contrast and providing an output image that is moreaesthetically pleasing to a viewer.

An example of how local tone mapping may be implemented in the presentembodiment is shown by way of example in FIGS. 138 and 139.Particularly, FIG. 138 depicts a conventional local tone mappingtechnique which may in some instances result in a limited output range,and FIG. 139 depicts an adaptive local tone mapping process that may beimplemented by the LTM logic 2202 that may make use of the full outputrange, even if a portion of input range is not used by the image frame.

Referring first to FIG. 138, the graph 2440 represents the applicationof local tone mapping to a higher bit-precision input image to produce alower bit-precision output image. For instance, in the illustratedexample, the higher bit-precision input image data may be 12-bit imagedata (with 4096 input values (e.g., values 0-4095)), as represented byrange 2442, that is tone mapped to produce an 8-bit output (with 256output values (e.g., 0-255)), represented here by range 2444. It shouldbe understood that the bit-depths are simply meant to provide examples,and should not be construed as limiting in any way. For instance, inother embodiments, the input image may be 8-bit, 10-bit, 14-bit, or 16bit, etc., and the output image may have a bit-depth that is greaterthan or less than 8-bit precision.

Here, it may be assumed that the region of the image on which local tonemapping is applied only utilizes a portion of the full input dynamicrange, such as the range 2448 represented by values 0-1023. For example,these input values may correspond to the values of the dark region 2434shown in FIG. 137. FIG. 138 shows a linear mapping of the 4096 (12-bit)input values to the 256 (8-bit) output values. Thus, while the valuesranging from 0-4095 are mapped to the values 0-255 of the output dynamicrange 2444, the unused portion 2450 (values 1024-4095) of the full inputrange 2442 is mapped to the portion 2454 (values 64-255) of the outputrange 2444, thereby leaving only the output values 0-63 (portion 2452 ofthe output range 2444) available for representing the utilized portion2448 (values 0-1023) of the input range. In other words, this linearlocal tone mapping technique does not take into account whether unusedvalues or ranges of values are mapped. This results in a portion (e.g.,2454) of the output values (e.g., 2444) being allocated for representinginput values that are not actually present in the region (e.g., 2434) ofthe image frame on which the present local tone mapping operation (e.g.,graph 2440) is being applied, thereby reducing the available outputvalues (e.g., 2452) that may be used to express the input values (e.g.,range 2448) present in the current region being processed.

With the foregoing in mind, FIG. 139 illustrates a local tone mappingtechnique that may be implemented in accordance with embodiments of thepresent disclosure. Here, prior to performing mapping of the input range2442 (e.g., 12-bit) to the output range 2444 (e.g., 8-bit), the LTMlogic 2202 may be configured to first determine a utilized range of theinput range 2442. For instance, assuming the region is a generally darkregion, the input values corresponding to color within that region mayonly utilize a sub-range, such as 2448 (e.g., values 0-1023), of thefull range 2442. That is, the sub-range 2448 represents the actualdynamic range present in the particular region of the image frame beingprocessed. Thus, since the values 1024-4095 (unused sub-range 2450) arenot being utilized in this region, the utilized range 2448 may first bemapped and expanded to utilize the full range 2442, as shown by theexpansion process 2472. That is, because the values 1024-4095 are notbeing utilized within the current region of the image being processed,they may be used to express the utilized portion (e.g., 0-1023). As aresult, the utilized portion 2448 of the input range may be expressedusing additional values, here approximately three times more additionalinput values.

Next, as shown by the process 2474, the expanded utilized input range(expanded to values 0-4095) may be subsequently mapped to the outputvalues 0-255 (output range 2444). Thus, as depicted in FIG. 139, as aresult of first expanding the utilized range 2448 of input values tomake use of the full input range (0-4095), the utilized range 2448 ofinput values may be expressed using the full output range 2444 (values0-255), rather than only a portion of the output range, as shown in FIG.138.

Before continuing, it should be noted that although referred to as alocal tone mapping block, the LTM logic 2202 may also be configured toimplement global tone mapping in some instances. For example, where theimage frame includes an image scene with generally uniformcharacteristics (e.g., a scene of the sky), the region on which tonemapping is applied may include the entire frame. That is, the same tonemapping operator may be applied to all pixels of the frame. Returning toFIG. 134, the LTM logic 2202 may also receive the data 2201 from theface detection logic 2200 and, in some instances, may utilize this datato identify one or more local areas within the current image frame towhich tone mapping is applied. Thus, the end result from applying one ormore of the above-described local tone mapping techniques may be animage that is more aesthetically pleasing to a viewer.

The output of the LTM logic 2202 may be provided to the brightness,contrast, and color adjustment (BCC) logic 2204. In the depictedembodiment, the BCC logic 2204 may be implemented generally identicallyto the BCC logic 1184 of the YCbCr processing logic 904 of the ISPpipeline, as shown in FIG. 132, and may offer generally similarfunctionality to provide for brightness, contrast, hue, and/orsaturation control. Thus, to avoid redundancy, the BCC logic 2204 of thepresent embodiment has not been re-described here, but should beunderstood to be identical to the previously described BCC logic 1184 ofFIG. 132.

Next, the scaling logic 2206 may receive the output of the BCC logic2204 and may be configured to scale the image data representing thecurrent image frame. For instance, when the actual size or resolution ofthe image frame (e.g., in pixels) is different from an expected ordesired output size, the scaling logic 2206 may scale the digital imageaccordingly to achieve an output image of the desired size orresolution. As shown, the output 126 of the scaling logic 2206 may besent to the display device 28 for viewing by a user or to memory 108.Additionally, the output 126 may also be provided to acompression/decompression engine 118 for encoding/decoding the imagedata. The encoded image data may be stored in a compressed format andthen later decompressed prior to being displayed on the display 28device.

Further, in some embodiments, the scaling logic 2206 may scale the imagedata using multiple resolutions. By way of example, when the desiredoutput image resolution is 720p (1280×720 pixels), the scaling logic mayscale the image frame accordingly to provide a 720p output image, andmay also provide a lower resolution image that may function as a previewor thumbnail image. For instance, an application running on the device,such as the “Photos” application available on models of the iPhone® orthe iPhoto® and iMovie® applications, available on certain models of theiPhone®, MacBook®, and iMac® computers, all available from Apple Inc.,may allow users to view a listing of preview-versions of video or stillimages stored on the electronic device 10. Upon selecting a stored imageor video, the electronic device may display and/or play back theselected image or video at full resolution.

In the illustrated embodiment, the scaling logic 2206 may also provideinformation 2203 to the back-end statistics block 2208, which mayutilize the scaling logic 2206 for back-end statistics processing. Forinstance, in one embodiment, the back-end statistics logic 2208 mayprocess the scaled image information 2203 to determine one or moreparameters for modulating quantization parameters associated with theencoder 118 (e.g., quantization parameters per macroblock), which may bean H.264/JPEG encoder/decoder in one embodiment. For instance, in oneembodiment, the back-end statistics logic 2208 may analyze the image bymacroblocks to determine a frequency content parameter or score for eachmacroblock. For instance, in some embodiments, the back-end statisticslogic 2206 may determine a frequency score for each macroblock usingtechniques such as wavelet compression, fast Fourier transforms, ordiscrete cosine transforms (DCTs). Using the frequency scores, theencoder 118 may be able to modulate quantization parameters to achieve,for example, a generally even image quality across the macroblocksconstituting the image frame. For instance, if a high variance in thefrequency content is present in a particular macroblock, compression maybe applied to that macroblock more aggressively. As shown in FIG. 134,the scaling logic 2206 may also provide a reduced resolution image,represented here by reference number 2207, to the face detection logic2200 by way of an input to the selection circuitry 2196, which may be amultiplexer or some other suitable type of selection logic. Thus, theoutput 2198 of the selection circuitry 2196 may be either the YCC input114 from the ISP pipeline 82 or the down-scaled YCC image 2207 from thescaling logic 2206.

In some embodiments, the back-end statistics data and/or the encoder 118may be configured to predict and detect scene changes. For instance, theback-end statistics logic 2208 may be configured to acquire motionstatistics. The encoder 118 may attempt to predict scene changes bycomparing motion statistics provided by the back-end statistics logic2208, which may include certain metrics (e.g., brightness), of a currentframe to a previous frame. When the difference in the metric is greaterthan a particular threshold, a scene change is predicted, the back-endstatistics logic 2208 may signal a scene change. In some embodiments,weighted predictions may be used, as a fixed threshold may not always beideal due to the diversity of images that may be captured and processedby the device 10. Additionally, multiple threshold values may also beused depending on certain characteristics of the image data beingprocessed.

As discussed above, the facial detection data 2201 may also be alsoprovided to the back-end statistics logic 2208 and the encoder 118, asshown in FIG. 134. Here, the back-end statistics data and/or the encoder118 may utilize the facial detection data 2201 along with macroblockfrequency information during back-end processing. For instance,quantization may be reduced for macroblocks that correspond to thelocation of faces within the image frame, as determined using the facialdetection data 2201, thus improving the visual appearance and overallquality of encoded faces and facial features present in an imagedisplayed using the display device 28.

Referring now to FIG. 140, a block diagram showing a more detailed viewof the LTM logic 2202 is illustrated in accordance with one embodiment.As shown, tone mapping is applied after first converting the YC1C2 imagedata 114 from the ISP pipeline 82 into a gamma corrected RGB linearcolor space. For instance, as shown in FIG. 140, logic 2208 may firstconvert the YC (e.g., YCbCr) data to a non-linear sRGB color space. Inthe present embodiment, the LTM logic 2202 may be configured to receiveYCC image data having different sub-sampling characteristics. Forinstance, as shown by the inputs 114 to a selection logic 2205 (e.g., amultiplexer), the LTM logic 2202 may be configured to receive YCC 4:4:4full data, YCC 4:2:2 chroma sub-sampled data), or YCC 4:2:0 chromasub-sampled data. For sub-sampled YCC image data formats, up-convertinglogic 2209 may be applied to convert the sub-sampled YCC image data toYCC 4:4:4 format before conversion by logic 2208 to the sRGB colorspace.

The converted sRGB image data, represented here by reference number2210, may then be converted into the RGB_(linear) color space, which isa gamma corrected linear space, by the logic 2212. Thereafter, theconverted RGB_(linear) image data 2214 is provided to the LTM logic2216, which may be configured to identify regions (e.g., 2432 and 2434of FIG. 137) in the image frame that share similar brightnesses and toapply local tone mapping to those regions. As shown in the presentembodiment, the LTM logic 2216 may also receive parameters 2201 from theface detection logic 2200 (FIG. 134) which may indicate the location andpositions within the current image frame where faces and/or facialfeatures are present.

After local tone mapping is applied to the RGB_(linear) data 2214, theprocessed image data 2220 is then converted back into the YC1C2 colorspace by first using the logic 2222 to convert the processedRGB_(linear) image data 2220 back to the sRGB color space, and thenusing the logic 2226 to convert the sRGB image data 2224 back into theYC1C2 color space. Thus, the converted YC1C2 data 2228 (with tonemapping applied) may be output from the LTM logic 2202 and provided tothe BCC logic 2204, as discussed above in FIG. 134. As will beappreciated the conversion of the image data 114 into the various colorspaces utilized within the ISP back-end LTM logic block 2202 may beimplemented using techniques similar to the conversion of the demosaicedRGB image data into the YC1C2 color space in the RGB processing logic902 of the ISP pipeline 82, as discussed above in FIG. 125. Further, inembodiments where the YCC is up-converted (e.g., using logic 2209), theYC1C2 data may be down-converted (sub-sampled) by the logic 2226.Additionally, in other embodiments, this sub-sampling/down-conversionmay also be performed by the scaling logic 2206 instead of the logic2226.

While the present embodiment shows a conversion process that convertsfrom the YCC color space to the sRGB color space and then to thesRGB_(linear) color space, other embodiments may utilize differencecolor space conversions or may apply an approximated transform using apower function. That is, in some embodiments, conversion to anapproximately linear color space may be sufficient for local tonemapping purposes. Thus, using an approximated transform function, theconversion logic of such embodiments may be at least partiallysimplified (e.g., by removing the need for color space conversionlook-up tables). In a further embodiment, local tone mapping may also beperformed in a color space that is perceptually better to the human eye,such as a Lab color space.

FIGS. 141 and 142 show flow charts that depict methods for processingimage data using the ISP back-end processing logic 120, in accordancewith disclosed embodiment. Referring first to FIG. 141, a method 2230generally illustrating the processing of image data by the ISP back-endprocessing logic 120 is depicted. Beginning at step 2232, the method2230 receives YCC image data from the ISP pipeline 82. For instance, asdiscussed above, the received YCC image data may be in the YCbCr lumaand chroma color space. Next, the method 2232 may branch to each ofsteps 2234 and 2238. At step 2234, the received YCC image data may beprocessed to detect positions/locations of faces and/or facial featureswithin a current image frame. For instance, with reference to FIG. 134,this step may be performed using the face detection logic 2200, whichmay be configured to implement a facial detection algorithm, such asViola-Jones. Thereafter, at step 2236, the face detection data (e.g.,data 2201) may be provided to the ISP control logic 84 as feedback tothe ISP front-end statistics processing units 142 or 144), as well as tothe LTM logic block 2202, the back-end statistics logic 2208, and theencoder/decoder logic 118, as shown in FIG. 134.

At, step 2238, which may occur at least partially concurrently with step2234, the YCC image data received from the ISP pipeline 82 is processedto apply tone mapping. Thereafter, the method 2230 continues to step2240, whereby the YCC image data (e.g., 2228) is further processed forbrightness, contrast, and color adjustments (e.g., using BCC logic2204). Subsequently, at step 2242, scaling is applied to the image datafrom step 2240 in order to scale the image data to one or more desiredsize or resolution. Additionally, as mentioned above, in someembodiments, color space conversion or sub-sampling may also be applied(e.g., in embodiments where YCC data is up-sampled for local tonemapping) to produce an output image having the desired sampling.Finally, at step 2244, the scaled YCC image data may be displayed forviewing (e.g., using display device 28) or may be stored in memory 108for later viewing.

FIG. 142 illustrates the tone mapping step 2238 of FIG. 141 in moredetail. For instance, the step 2238 may begin with sub-step 2248, inwhich the YCC image data received at step 2232 is first converted to thesRGB color space. As discussed above and shown in FIG. 140, someembodiments may provide for up-conversion of sub-sampled YCC image databefore conversion to the sRGB space. Thereafter, the sRGB image data isconverted to a gamma-corrected linear color space, RGB_(linear), atsub-step 2250. Next, at sub-step 2252, tone mapping is applied to theRGB_(linear) data by the tone mapping logic 2216 of ISP back-end LTMlogic block 2202. The tone mapped image data from sub-step 2252 may thenbe converted from the RGB_(linear) color space back to the sRGB colorspace, as shown at sub-step 2254. Thereafter, at sub-step 2256, the sRGBimage data may be converted back to the YCC color space, and step 2238of the method 2230 may continue to step 2240, as discussed in FIG. 141.As mentioned above, the process 2238 shown in FIG. 142 is merelyintended to be one process for applying color space conversion in amanner suitable for local tone mapping. In other embodiments,approximated linear conversions may also be applied in place of theillustrated conversion steps.

As will be understood, the various image processing techniques describedabove and relating to defective pixel detection and correction, lensshading correction, demosaicing, and image sharpening, among others, areprovided herein by way of example only. Accordingly, it should beunderstood that the present disclosure should not be construed as beinglimited to only the examples provided above. Indeed, the exemplary logicdepicted herein may be subject to a number of variations and/oradditional features in other embodiments. Further, it should beappreciated that the above-discussed techniques may be implemented inany suitable manner. For instance, the components of the imageprocessing circuitry 32, and particularly the ISP front-end block 80 andthe ISP pipe block 82 may be implemented using hardware (e.g., suitablyconfigured circuitry), software (e.g., via a computer program includingexecutable code stored on one or more tangible computer readablemedium), or via using a combination of both hardware and softwareelements.

The specific embodiments described above have been shown by way ofexample, and it should be understood that these embodiments may besusceptible to various modifications and alternative forms. It should befurther understood that the claims are not intended to be limited to theparticular forms disclosed, but rather to cover all modifications,equivalents, and alternatives falling within the spirit and scope ofthis disclosure.

1. An image signal processing system comprising: an image processingpipeline configured to receive a frame of raw image data comprisingpixels acquired using a digital image sensor and to process the rawimage frame by applying demosaicing to produce a corresponding frame offull color RGB image data and subsequently convert the RGB image datainto a corresponding frame of luma and chroma image data; and a back-endprocessing unit configured to receive the frame of luma and chroma imagedata, and to process the luma and chroma image frame using at least oneof feature detection logic, tone mapping logic, and scaling logic. 2.The image signal processing system of claim 1, wherein the featuredetection logic comprises face detection logic configured to detectlocations within the received frame containing facial features, and toprovide the detected locations as a set of facial detection data.
 3. Theimage signal processing system of claim 2, wherein the face detectionlogic is configured to detect the locations of the facial features usingan object detection algorithm.
 4. The image signal processing system ofclaim 1, wherein the feature detection logic is configured to detectlocations within the received frame that correspond to corners ofobjects in order to determine global between the received frame and atleast one prior or subsequent frame.
 5. The image signal processingsystem of claim 2, comprising a front-end processing unit coupledupstream from the image processing pipeline and having a front-endstatistics collection engine configured process the raw image frame todetermine at least one of auto-white balance statistics, auto-exposurestatistics, auto-focus statistics, and flicker detection statisticsprior to the raw image frame being processed by the image processingpipeline and the back-end processing unit.
 6. The image signalprocessing system of claim 5, wherein the facial detection data is fedback to the front-end statistics collection engine, and wherein thefront-end statistics collection engine is configured to position one ormore statistics collection windows for determining auto-white balancestatistics using the facial detection feedback data.
 7. The image signalprocessing system of claim of claim 1, wherein the digital image sensorcomprises a Bayer color filter array, and wherein the raw image datacomprises Bayer image data.
 8. A method for processing image data usingback-end processing logic of an image signal processing (ISP) systemcomprising: receiving an image frame comprising luma and chroma imagedata, the image frame having been previously processed by an imageprocessing pipeline of the ISP system; applying local tone mapping tothe image frame using tone mapping logic; and using scaling logic toscale the image frame to at least one desired output resolution afterapplying local tone mapping.
 9. The method of claim 8, wherein applyinglocal tone mapping to the image frame comprises dividing the image frameinto sections based on local features within the image frame andprocessing each of the sections by: identifying a total availabledynamic range of the image frame; identifying a total available outputdynamic range; determining the actual dynamic range of luma values in acurrent section of the image frame; expanding the actual dynamic rangeof the current section by mapping the actual dynamic range to the totalavailable dynamic range if the actual dynamic range is less than thetotal available dynamic range; and mapping the expanded actual dynamicrange to the total available output dynamic range.
 10. The method ofclaim 9, wherein applying local tone mapping comprises using local tonemapping operators.
 11. The method of claim 10, wherein local tonemapping is applied in at least one of an RGB linear color space, a Labcolor space, or some combination thereof.
 12. The method of claim 8,comprising applying at least one of brightness, contrast, and color(BCC) adjustments to the image frame using BCC adjustment logic afterapplying local tone mapping but before applying scaling using thescaling logic.
 13. The method of claim 8, wherein the at least onedesired output resolution comprises a first output resolution and asecond output resolution, wherein the first output resolutioncorresponds to a desired viewing resolution of a display deviceconfigured to display the image frame, and wherein the second outputresolution corresponds to a preview resolution that is lower than thedesired viewing resolution.
 14. An image signal processing systemcomprising: a back-end pixel processing unit configured to receive aframe of image data, wherein the back-end pixel processing unitcomprises: face detection logic configured to receive and process theimage frame and to output set of facial detection data indicative of thelocation of facial features within the image frame; tone mapping logicconfigured to applying tone mapping to the image frame; scaling logicconfigured to scale the image frame to one or more desired outputresolutions; and a back-end statistics processing unit configured toprocess the scaled image data to collect frequency statistics for thescaled image data; and encoder/decoder logic configured to applycompression to the scaled image data based at least partially upon thefrequency statistics.
 15. The image signal processing system of claim14, wherein the back-end statistics processes the scaled image data as aplurality of macroblocks, and wherein collecting the frequencystatistics comprises determining a frequency parameter indicative of thefrequency content for each macroblock.
 16. The image signal processingsystem of claim 15, wherein the frequency parameter is determined foreach macroblock using at least one of a discrete cosine transform, afast Fourier transform, or wavelet compression, or some combinationthereof.
 17. The image signal processing system of claim 15, wherein,for each macroblock, the encoder/decoder logic modulates at least onequantization parameter based upon the frequency parameter associatedwith the macroblock.
 18. The image signal processing system of claim 14,wherein the facial detection data is provided to at least one of theback-end statistics processing unit and the encoder/decoder logic, andwherein the encoder/decoder logic is configured to reduce quantizationfor macroblocks in the image frame that correspond to the locations offacial features, as indicated by the facial detection data.
 19. Theimage signal processing system of claim 14, wherein the back-end pixelprocessing unit comprises selection logic configured to receive both theimage frame and at least one scaled image frame from the scaling unit,and wherein the selection logic is configured select one of either theimage frame or the scaled image frame and to provide the selected imageframe to the face detection logic.
 20. The image signal processingsystem of claim 14, wherein the encoder comprises an H.264/JPEG encoder.21. An electronic device comprising: a digital image sensor; a sensorinterface configured to communicate with the digital image sensor; amemory device; a display device configured to display a visualrepresentation of an image scene corresponding to raw image dataacquired by the digital image sensor; and an image signal processingsub-system comprising: an image processing pipeline configured toreceive a frame of raw image data comprising pixels acquired using thedigital image sensor, demosaic the raw image frame to produce acorresponding full color RGB image frame, and convert full color RGBimage data into a corresponding luma and chroma image frame; and aback-end processing unit configured to receive and process the luma andchroma image frame using at least one of face detection logic, localtone mapping logic, and scaling logic, and to collect image statisticsusing a back-end statistics engine; wherein the image statistics includefrequency statistics which may be used to determine quantizationparameters for encoding the luma and chroma image frame using anencoder.
 22. The electronic device of claim 21, wherein processing theluma and chroma image frame using the local tone mapping logic comprisesfirst converting the luma and chroma frame from the luma and chromacolor space into an at least approximately linear RGB color space, andapplying local tone mapping to the approximately linear RGB color space.23. The electronic device of claim 22, wherein converting the luma andchroma data into the at least approximately linear RGB color spacecomprises applying an approximated transform based upon a powerfunction.
 24. The electronic device of claim 23, wherein if the luma andchroma data comprises luma and chroma data in a YCC 4:2:2 or YCC 4:2:0sub-sampled data format, the luma and chroma data is first upconvertedto a YCC 4:4:4 full data format before being converted into the at leastapproximately linear RGB color space.
 25. The electronic device of claim22, wherein converting the luma and chroma data into the at leastapproximately linear RGB color space comprises converting the luma andchroma data to an sRGB color space, and then converting the sRGB data toan sRGB_(linear) color space.
 26. The electronic device of claim 21,wherein the digital image sensor comprises at least one of a digitalcamera integrated with the electronic device, an external digital cameracoupled to the electronic device via an input/output port, or somecombination thereof.
 27. The electronic device of claim 21, wherein thesensor interface comprises a Standard Mobile Imaging Architecture (SMIA)interface, a serial imaging interface, a parallel imaging interface, orsome combination thereof.
 28. The electronic device of claim 21,comprising at least one of a desktop computer, a laptop computer, atablet computer, a mobile cellular telephone, a portable media player,or any combination thereof.
 29. The electronic device of claim 21,wherein the image statistics comprise motion statistics, and wherein theback-end processing unit is configured to perform scene detection basedupon the motion statistics.